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Land use change detection in the limestone exploitation area of Margalla Hills National Park (MHNP), Islamabad, Pakistan using geo-spatial techniques

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The aim of the study is to assess the limestone (LS) exploitation area and its negative impacts on natural resources using geo-spatial techniques. LS is an important constituent of cement manufacturing which is extensively used in the construction of infrastructure such as roads and buildings. The Margalla Hills National Park (MHNP) is situated around Islamabad and contains a large amount of LS reserves. However, the exploitation of LS from the MHNP is causing harmful environmental impacts on the surrounding areas. Geographic Information System (GIS) and Remote Sensing (RS) have been proved as powerful tools for LS exploitation assessment. Four Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) satellite images have been acquired over a span of 17 years (1992-2009). The temporal changes in the study area were detected by performing the digital image processing techniques of image enhancement and supervised classification. The classification accuracy has been verified with high resolution Google Earth images and by using error matrix. Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation (GDEM) has been used for topographic information extraction. It was observed that LS exploitation area increased from 0.35% to 5.72% from 1992-2009, whereas, the vegetation was decreased from 23.46% to 12.12%. Urban development also increased rapidly. The results showed that LS exploitation is deteriorating the ecosystem, biodiversity, landscape and vegetation of the MHNP which was established in 1980 to protect, conserve and manage the biodiversity and ecosystem in this region. We conclude that the LS exploitation in the MHNP should be managed properly to secure the water, soil, air quality in the federal capital. Initiatives should be taken for the rehabilitation of the LS exploited areas and then, to suggest alternative LS exploitation sites in the near periphery of study area with EIA restrictions.
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89
Journal of Himalayan Earth Sciences 46(1) (2013) 89-98
Land use change detection in the limestone exploitation area of Margalla Hills
National Park (MHNP), Islamabad, Pakistan using geo-spatial techniques
Muhammad Farooq Iqbal1, Mobushir Riaz Khan1 and Amir H. Malik2
1Department of Meteorology, COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan
2Department of Environmental Sciences, COMSATS Institute of Information Technology (CIIT),
Abbottabad, Pakistan
Abstract
The aim of the study is to assess the limestone (LS) exploitation area and its negative impacts on
natural resources using geo-spatial techniques. LS is an important constituent of cement manufacturing
which is extensively used in the construction of infrastructure such as roads and buildings. The Margalla
Hills National Park (MHNP) is situated around Islamabad and contains a large amount of LS reserves.
However, the exploitation of LS from the MHNP is causing harmful environmental impacts on the
surrounding areas. Geographic Information System (GIS) and Remote Sensing (RS) have been proved as
powerful tools for LS exploitation assessment. Four Landsat Thematic Mapper (TM)/Enhanced Thematic
Mapper (ETM+) satellite images have been acquired over a span of 17 years (1992-2009). The temporal
changes in the study area were detected by performing the digital image processing techniques of image
enhancement and supervised classification. The classification accuracy has been verified with high
resolution Google Earth images and by using error matrix. Advanced Space borne Thermal Emission and
Reflection Radiometer (ASTER) Global Digital Elevation (GDEM) has been used for topographic
information extraction. It was observed that LS exploitation area increased from 0.35% to 5.72% from
1992-2009, whereas, the vegetation was decreased from 23.46% to 12.12%. Urban development also
increased rapidly. The results showed that LS exploitation is deteriorating the ecosystem, biodiversity,
landscape and vegetation of the MHNP which was established in 1980 to protect, conserve and manage
the biodiversity and ecosystem in this region. We conclude that the LS exploitation in the MHNP should
be managed properly to secure the water, soil, air quality in the federal capital. Initiatives should be taken
for the rehabilitation of the LS exploited areas and then, to suggest alternative LS exploitation sites in the
near periphery of study area with EIA restrictions.
Keywords: Limestone exploitation; Landsat satellite; Remote Sensing; Land use; Change detection
1. Introduction
Limestone (LS) exploitation is carried out for
the cement production which is obtained by grinding
clinker with Gypsum and used for construction
purposes (Akinbile, 2007). Stone crushing and
cement production is one of the significant industrial
activities that exists all over the world. This sector is
an important industrial segment for infrastructural
development and construction of roads, bridges,
canals, dams, building and housing projects.
However, during the process of crushing a
considerable amount of dust is emitted at almost
every stage (Akinbile, 2007). This process not only
effects the local environment but also the human
health in the surrounding areas. Furthermore, the
removal of vegetation exposes soil surface and
thereby enhances the chances of land erosion
(Ibrahim, 2002). Therefore, LS exploitation must
involve planning, conflict resolution, construction,
operation and close down (FMSMD, 1999).
Margalla hills contain high quality of
Limestone (LS including sand-stone and shale,
which is considered as best for construction
purposes (Nawaz et al., 2004). Increasing trend of
LS exploitation is harmful for vegetation, natural
ecosystem, biodiversity and human health. Thus,
environmental impact assessment (EIA) is required
before the execution of any project to mitigate the
harmful environmental impacts. Conventional
surveying and mapping techniques consume a lot
of time and are expensive as well for the
assessment of LS exploitation area while such
90
information is not readily available, especially in
developing countries. Satellite Remote Sensing
(SRS) and GIS have been widely applied in
identifying and analyzing for LS exploitation
assessment, environmental impact assessment, geo-
morphological analysis, land use mapping and
planning (Treitz et al., 1992; Harris and Ventura,
1995). Usually, remotely sensed data is used to
provide information on terrain surface whereas on
the other hand, GIS is a supporting tool to RS and
has the capability to manipulate and store data in
digital forms (Shaban, 2010). Using GIS,
Wandahwa and van (1996) implemented land
suitability evaluation and mapped climate, altitude,
soil type, and ecosystem. The topography of the
land surface is one of the most essential
characteristic of the earth used in GIS analysis
(Reuter et al., 2009).
Space-borne sensors have shown a great
potential for delivering reliable estimates of the
extent and changes occurring in the LS exploitation
area along with mapping urban growth and forest
cover estimation (Foody et al., 1996). Multi-
temporal and multi-spectral satellite images are
used for characterizing land use and land cover
(LULC) change and deforestation rates (Lu et al.,
2004). SRS is a growing technology and is being
used as a fundamental component of conservation
planning and biodiversity assessment (Sesnie et al.,
2008; Stickler and Southworth, 2008). LS
exploitation area assessment from satellite data is
executed using either supervised and/or
unsupervised classification method. The spectral
complexity of predefined classes has further led to
numerous suggestions for procedures and
techniques to improve classifications including
topographic normalization, spatial filtering, image
segmentation, object-oriented classifications
(Foody et al., 1996), vegetation indices and multi-
temporal image data (Lucas et al., 1993).
LULC changes may have negative impacts on
environment, water resources, land and vegetation
if appropriate measures are not taken in time
(Sunday and Ajewole, 2006).The lack of proper
rehabilitation of the LS exploitation sites causes
serious environmental degradation and spread of
diseases (Kaliampakos et al., 1998). This
necessitates temporal monitoring of changes
occurring in the land use and land cover of an area.
The need for low-cost data resource is particularly
important for conservation and research in
developing countries where funding for mapping is
often limited. So research interests are being
directed to the mapping and monitoring using
RS/GIS techniques (Epstein et al., 2002). Landsat
onboard Multispectral Scanner (MSS), Thematic
Mapper (TM), and Enhanced Thematic Mapper
Plus (ETM+) is archiving and delivering free of
costimagesforabout30 years (USGS, 2008). It
meets a wide range of information requirements for
monitoring the conditions of earth’s land surface
(Williams et al., 2006; Chander et al., 2009). The
availability of multispectral and high resolution
data as well as advanced capabilities of digital
image processing techniques, in generating
enhanced and interpretable images, has further
enlarged the potential use of RS in delineating
lithological contacts and geological structure in
great detail (Drury, 1987; Yousif and Shedid, 1999;
Crippen and Blom, 2001). There are many
applications of RS in geology which involve the
delineation of structures, discrimination of different
rock, soil types and resource exploration (Kruse
and Dietz, 1991). Many geological studies have
employed TM and ETM+ data to discriminate the
various lithologies, lineaments limestone and
minerals (Abrams, 1984).
The main objective of the current study is to
assess the spatiotemporal changes in the limestone
exploitation area and its impacts over the past 17
years, using geospatial technique. Moreover, to
identify alternative LS querying sites in an attempt
to save the MHNP.
2. Study area
Margalla Hills National Park (MHNP) is
located in the north of the Pakistan’s capital city
Islamabad, whereas the study area lies between
33040'01" to 33042'43" N latitude, 72045'01" to
72052'32"E longitude as shown in (Fig. 1). MHNP
was declared as the National Park under
Islamabad Wild Life Ordinance in 1980. The step
was taken to conserve the natural resources from
injudicious human activities such as over
cultivation, grazing, mining and water pollution
(Ahmad, 2009). The foothills run approximately
from north to northwest direction and are about 40
km in length (Malik and Husain, 2003). The
current study focuses on a subpart of MHNP, where
limestone exploitation is taken place. MHNP, like
91
the Potwar Plateau and Azad Kashmir, also has a
distinct altitudinal range and lies at the junction of
Potwar Plateau and northern mountainous region of
Pakistan (Masroor, 2011). The soil of the study area
is derived from wind, water-laid deposits and
sedimentary rocks. Margalla Hills are largely
tertiary in age with smaller areas of formation
belonging to quartzitic sandstone calcareous shale
and limestone (Hijazi, 1984). The topography of
the area is rugged, varying in elevation comprising
mainly steep slopes and gullies (Fig. 2), where the
rock structure is basically limestone (Yasin and
Rubina, 1987). Natural springs and rainfall play an
important role for streams in the MHNP. The area
falls in the far end of monsoon zone and the mean
monthly 254 mm of monsoon precipitation occurs
in July and August (Maqsood, 1991). The mean
relative humidity for the same period varies
between 59 and 67% (Masroor, 2011). The hottest
months are May and June as the temperature then
rises up to 42°C and the coldest months are
December and January when temperature falls
below zero (Hussain, 1986). MHNP vegetation is
largely the result of monsoon and the foothills flora
is mostly tropical in origin (Shinwari and Khan,
1998). According to Chandio (1995) the water-
table has dropped from 12.5 to 22.9m in
Rawalpindi and from 10 to 19.8m in Islamabad.
Fig. 1. The location of the study area in the Margalla Hills National Park, Islamabad (Pakistan)
Fig.2. Digital Elevation Model (DEM) generated and extracted for topographic analysis, where study
area is divided into six zones with different elevations ranging from 449 to 883 meters.
92
3. Data and Methodology
Two types of data have been used in the
study. Elevation data is used for topographic
information extraction and multi-temporal
Landsat satellite images were used for Limestone
exploitation area assessment. Other LULC classes
i.e. bare land, vegetation, water, road network and
urban development were derived to observe the
impact of limestone exploitation on them.
Advanced Space borne Thermal Emission and
Reflection Radiometer (ASTER) Global Digital
Elevation Model (GDEM) have been released
with a 1 arc second and provided much extended
coverage. ASTER GDEM is an easy-to-use,
covering all the land on earth, and available to all
users regardless of size or location of the study
areas to extract topographic information. The
ASTER GDEM with 30 meter resolution is
expected to meet the requirements of many users,
for global topographic information (Reuter et al.,
2009).
Landsat data is a precious resource for
monitoring global changes and is a primary source
of medium spatial resolution used in decision-
making (Goward et al., 2006; Masek et al., 2008;
Vogelmann et al., 2008). Remote sensing
classification approaches such as supervised,
unsupervised and knowledge-based expert system
approaches (Sugumaran et al., 2003; Mundia and
Aniya, 2005; Lu and Weng, 2005), have been
used and are still being used for the land cover
change detection. The supervised classification is
a time consuming, however, an accurate system
used to extract surface features manually from
remote sensing images (Butt et al., 2011).
Heavy LS exploitation started in the study
area in 1980’s. This study is based on land cover
classification of four multi-temporal cloud free
images. The acquisition dates are 20 September
1992, 08 September 1998, 21 May 2002 and 30
May 2009 with having path 150 and row 37.
ERDAS Imagine 9.0 software was used to carry
out all image processing tasks while Arc GIS 9.1
was used for analysis and map development. To
minimize radiometric distortion due to different
atmospheric conditions and different acquisition
dates, a basic radiometric and image-based
atmospheric correction according to Chavez
(1996) was applied. Radiometric correction was
performed on all the four images due to variations
in scene illumination and sensor irregularities,
viewing geometry, atmospheric conditions and
sensor noise. The images have been geometrically
corrected using the UTM map projection (Zone
43N, datum WGS84). In order to geometrically
correct the original distorted image, the
resampling technique of nearest neighbor method
is used to determine the digital values, for placing
in the new pixel locations of the corrected output
image. This method uses the digital value from
the pixel in the original image which is average of
four nearest to the new pixel location in the
corrected image with RMS error of less than one
pixel. Our methodology is based on the Principal
Component Analysis (PCA) and supervised
classification methods. PCA are used to extract
specific training sites and finally, the Maximum
Likelihood (MLA) supervised classification
algorithmis used to produce satellite-derived maps
(Butt et al., 2012). PCA is used to convert raw
remote sensing data of multi-spectral imageries
into a new principal component image, which is
more easily interpretable (Singh and Harrison,
1985; Weng, 1993). Digital image processing
techniques are used to define unique training sites
for classification of the study area. The image
classification procedure is applied to automatically
categorize all pixels into land cover classes or
themes on the basis of training sites defined in an
image. In this study 40 training sites are identified
on the basis of filed survey and image
interpretation. The Maximum Likelihood (MLH)
classification algorithm is applied in the current
study, because it can be more effective than the
often used Minimum Distance Algorithm (MDA)
and Mahalanobis Distance Algorithm (MDA)
when the number of training sites per class is
larger (Jensen, 1996). As for as the spectral
reflectance concerns, water bodies generally
reflect high in the visible spectrum. For
vegetation, the spectral reflectance is based on the
chlorophyll and water absorption in the leaf. For
man-made materials, concrete and asphalt, both
display spectral curves generally increase from the
visible through the Near IR and Mid-IR regions.
Bare land decreases as organic matter increases
(Iqbal et al., 2009).
The classification accuracy was verified using
high resolution Google Earth images which are
available and quite conveniently help to recognize
93
the studied natural objects. The study area shape
file was converted into KML layer which was
then imported to Google Earth. The classified
images and Google Earth images, all were
displayed simultaneously to cross refer the
classification. The areas that indicated limestone,
vegetation, urban development, water and bare
land on the classified images were zoomed in the
Google Earth images covered by KML layer and
the classification accuracy was checked. Accuracy
assessment was also performed by using the
original satellite images to avoid errors in the
reference data using Error matrix. Error matrices
were developed on all the four images to check
the accuracy as shown in Table 1. The
classification accuracy using error matrix was
found to be more than 98%. The higher level of
accuracy was obtained in the data of TM 1992 and
less accuracy was found in the data of TM 2009.
With the help of field survey and literature
review alternative sites would be identified and
their quantity and quality would be examined.
Few alternative querying sites have also been
recommended by the (Nawaz et al., 2004) other
than MHNP.
Table 1. Accuracy analysis (percentage) of all the classes derived from Landsat data.
Classes
Limestone
Exploitation
Vegetation
Water
Bare
land
Urban
Development
Road
Network
Limestone Exploitation
98.25
0
0
0.67
0
0
Vegetation
0
99.46
0
0
0
0
Water
0
0
100
0
0
0
Bare land
1.09
0
0
99.33
0.32
0
Urban Development
0.66
0.54
0
0
99.68
0.07
Road Network
0
0
0
0
0
99.93
Limestone Exploitation
99.53
0
0
0.47
0.88
0
Vegetation
0
99.82
0
0
0
0
Water
0
0
100
0
0
0
Bare Land
0.47
0
0
99.01
1.23
0
Urban Development
0
0.18
0
0.52
97.89
0
Road Network
0
0
0
0
0
100
Limestone Exploitation
98.95
0
0
0
0
0
Vegetation
0
99.73
0.48
0
0
0.99
Water
0
0
99.52
0
0
0
Bare land
1.05
0
0
98.01
0
0
Urban Development
0
0.04
0
1.51
100
0
Road Network
0
0.23
0
0.48
0
99.01
Limestone Exploitation
99.29
0
0
0
1.27
0
Vegetation
0
99.02
0
0.02
0
0
Water
0
0
100
0
0
0
Bare Land
0.71
0
0
99.95
0
0
Urban Development
0
0
0
0.03
98.73
0
Road Network
0
0.98
0
0
0
100
94
4. Results and discussion
Landsat TM and ETM+ satellite data was
classified using MLH algorithm to investigate the
LS exploitation area. Results of this classification
are shown in Table 2 and Figure3. Results show
that the LS exploitation area is increasing rapidly
from the past two decades. It can be deduced
from the results that in a short span of six years
from September 1992 to September 1998, the LS
exploitation area was increased by 1.47% (Fig. 4)
whereas, LS exploitation area increased by 3.08%
from September 1998 to May 2000. From May
2000 to May 2009 the increase observed 5.72%.
From the past two decades, we observed a
continuous increase in the LS exploitation area
and if it continues with the same rate in the
coming years, then some part of the Margalla
Hills can be disappeared. Prominent agencies of
the country including Environmental Protection
Agency (EPA) and Natural Resource Monitoring
(NRM) can be benefitted with such studies in
order to make plans for better management of the
limestone exploitation. The rapidly occurring LS
exploitation is in turn severely destroying the
natural ecosystem and vegetation as the results
shows that the vegetation is decreasing rapidly in
the study area. Total vegetation-covered area;
either sparse or dense decreased by about 12%
from September 1992 to 2009 (Table 2). The
obvious effect of LS exploitation would be on
vegetation and decreasing trend of vegetation
would result in bare land. Urban development
increased very rapidly in the study area from 1992
to 2009 as in September 1992 it was recorded to
be 0.44%, and finally in May 2009 urbanization was
estimated to be 3.86%. We monitored increasing and
decreasing trend of bare land in various years due to
other factors including vegetation, limestone
exploitation area, urbanization and water.
Increasing and decreasing trend of road network
has been found to be due to the condition of the
road. If the road would be repaired, then it would
not have been reflected as road and considered as
bare land. In this study, only surface water was
considered as water class and changing in the
water is due to the over consumption of water.
Water is directly related to the rainfall. If there
would have been rainfall prior to taking of image,
then we could have been observed more water-
covered area. Maps of the study area after
classification process are shown in Figures 5.
Table 2. Area in square kilometer (km2) obtained from Landsat satellite of the classes including
Limestone Exploitation, Bare Land, Vegetation, Water, Road network and Urban development.
A comparison was made between every two years to find the changes annually.
Class
1992
1998
Annual
changes
(1992-1998)
2002
Annual
changes
(1998-2002)
2009
Annual
changes
(2002-2009)
Limestone Exploitation
00.35
01.47
00.19
03.08
00.40
05.72
00.38
Bare Land
18.26
26.90
01.44
21.31
-01.40
20.18
-00.16
Vegetation
23.46
12.57
-10.89
15.10
00.63
12.12
-00.43
Water
00.33
00.75
00.07
00.17
-00.15
00.18
00.01
Road Network
00.45
00.09
-00.06
00.45
00.09
01.10
00.09
Urban Development
00.44
01.39
00.16
02.96
00.39
03.86
00.13
95
Fig. 3. Increasing and decreasing trend of all the six classes from 1992 to 2009, where limestone
exploitation area is increasing rapidly with the increasing speed of urbanization.
Fig. 4. Change detection of all the classes in percentage from 1992 and 2009.
0
5
10
15
20
25
30
Limestone
Exploitation Bare Land Vegetation Water Road network Urban
Development
1992 1998 2002 2009
-12
-10
-8
-6
-4
-2
0
2
4
Limestone
Exploitation Bare Land Vegetation Water Road
network Urban
Development
Changes (1992-1998) Changes (1998-2002) Changes (2002-2009)
96
(a) 1992
(b) 1998
(c) 2002
(d) 2009
Fig. 5. Classified maps of satellite image from the year 1992 to 2009 (a-d).
Results show overall 98% average
classification accuracy using error matrices and
these results were also verified through high
resolution Google Earth images that are available
and quite conveniently help to recognize the
studied objects in the study area.
We have also identified various LS querying
sites other than the MHNP. These sites have been
identified using field survey and comprehensive
literature review. The alternative LS querying sites
are Khairi Murat, Kala ChittaDhar, Pathargarh,
Khanpur and Ganghar Ranges. It has been analyzed
that the recommended sites are sufficient for not
only present needs but also for future requirements.
These alternatives sites are not far away from
Islamabad and Rawalpindi and enough for present
and future requirements. Alternative recommend
sites would be beneficial to save the biodiversity
and ecosystem of MHNP and also support the
Government to collect the revenue.
5. Conclusion
On the basis of our results, we conclude that
i. Limestone exploitation is directly
affecting the vegetation-covered area that
might accelerate erosion.
ii. Due to excessive LS exploitation,
environment is also getting polluted and
as a result it is also infecting the
surrounding areas other than study area.
iii. LS exploitation is altering the natural
ecosystem and biodiversity.
iv. Geospatial techniques can be used to
assist the policy makers to identify
alternate sites for LS exploitation.
v. If both the LS exploitation and
urbanization keep on increasing with the
same rate, then environment of the study
area would be rigorously polluted.
97
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... The terrain is undulating and rough, consisting of steep slopes and gullies with altitudes from 465 to 1600 m a.s.l. (Iqbal et al. 2013). The rock base belongs to the Paleocene to Eocene period with the foundation of limestone (Iqbal et al. 2013;Khalid and Saeed Ahmad 2015). ...
... (Iqbal et al. 2013). The rock base belongs to the Paleocene to Eocene period with the foundation of limestone (Iqbal et al. 2013;Khalid and Saeed Ahmad 2015). The rock colour is greenish-brown and the calcareous substance is grayish blue. ...
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... In the signature file, the signature classes are being stored. Following the creation of the signature file, the images were classified by using maximum likelihood classification where the users initially determine the types and numbers of LULC classes (Iqbal et al., 2013). It quantifies the variance and covariance of the signature classes spectral response before classifying the unknown pixel into the given meaningful classes (Olsson, 1979). ...
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... In the signature file, the signature classes are being stored. Following the creation of the signature file, the images were classified by using maximum likelihood classification where the users initially determine the types and numbers of LULC classes (Iqbal et al., 2013). It quantifies the variance and covariance of the signature classes spectral response before classifying the unknown pixel into the given meaningful classes (Olsson, 1979). ...
Article
Full-text available
The goal of this research is to employ remote sensing to assess the influence of the Chashma Right Bank Canal (CRBC) on land-use/land-cover (LULC) changes and cropping patterns in the Dera Ismail Khan District of Khyber Pakhtunkhwa, Pakistan. LULC changes and cropping patterns were quantified using multi-temporal Landsat images from 1990 to 2018. Our finding revealed that between 1990 and 2018, agriculture, build-up, and water bodies increased by 52.22%, 5.44%, and 2.06% respectively, at the cost of decreasing barren land (50.27%), sand (5.05%), and shrubland (4.41%). Similarly, between 1997 and 2017, crop types such as sugarcane, and rice field increased by 19.77%, and 1.79%, whereas wheat decreased by 22.23%. We found that CRBC significantly altered the LULC of the study area, resulting in favorable changes in land-cover and cropping patterns, emphasizing the relevance of irrigation projects.
... Providing science-based solutions through determining the pace and magnitude of land use and land cover change, can support policymakers for future development and action plans (Mazeka et al. 2022). In African countries' economies which are mostly resource-based, the dynamics and intensity at which the landscapes of cities are transformed are shaped by the governance system, the chosen pathways of development (shared socio-economic pathways, overshoot/non-overshoot, mitigation, or adaptation) (Heynen et al. 2006), population growth and human settlements expansion (Iqbal et al. 2013). ...
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This study uses remote sensing and GIS techniques to examine the intensity and dynamics of land use/cover change and environmental indices across a four-decade period in the Chingola district of Zambia, from 1972 to 2020 using five classification stages (1972, 1992, 2001, 2013, and 2020). A total of 10 key climate change detection monitoring indices were generated using RClimDex to examine the implications of land degradation on the bioclimatic factors from 1983 to 2020. The findings revealed a significant expansion in Built-ups (7.3%/year), farmlands (3.18%/year), and mining areas (0.82%/year) at the expense of natural resources. The highest human pressure was exerted on Savannah woodlands (À0.78), through agriculture (0.76) and infrastructure development (0.44) between 1992 and 2001.The analysis of the bioclimatic indices revealed a significant decline in rainfall quantity and intensity, and a rising in temperature (warmer days and nights). The Annual rainfall has decreased by À3.25%, while the potential evapotranspiration has increased by 0.04% from 1983 to 2020, resulting in an Aridity Index of 0.60 and a moisture deficit index of À0.42. To offset agriculture's propensity to spatially expand and further encroach into savannah woodlands and forests , urban containment policies and programs that stimulate agricultural intensification are needed to reduce urban sprawl and protect the city's remaining forestlands. HIGHLIGHTS The most significant changes in LULC in Chingola occurred between 2001 and 2013 with a CLUDI of (623). It was observed that the days and nights are becoming warmer given the trend TX90p and TN90p. The PET analysis showed the years A declining trend was observed in NDVI, NDWI, NDMI, and NDSI over the study period (1972-2020). The highest human pressure was exerted on Savannah woodlands with an urban sprawl index of (À0.78). The year 1998 was identified as the hottest and driest of 1983-2020 timeseries.
... are the natural and primary causes for the ignition and spread of wildfires, while anthropogenic causes, including disturbances to woodlands, coming in second place in reports of past fire incidents [113]- [115]. ...
Thesis
Forest fires appear unavoidable in the natural world and they play a vital role in the regeneration of flora and the change of ecosystems. Nonetheless, unregulated forest fires may have detrimental environmental and local impacts. These fires are not only harmful for property and human life but also endanger ecosystem permanency. There has been a growing increase in the amount and intensity of forest fires across the world over the last decade. This phenomenon raises public concern about the environmental and socio-economic impacts of forest fires. Remote sensing and geographic information systems (RS and GIS) are useful instruments for acquiring data quickly and accurately describing the forest environment following a forest fire. The goals of this study were to evaluate the ability of optical and radar satellite remotely sensed data to monitor, extent, mapping of fire-prone areas, estimating the severity and intensity of forest fires and distinguish forest damage ( the burned area) caused by fire, as well as to establish a spatial model for forest fire threats. A number of methods were adopted for the identification of causes of forest fires, extraction of burnt and un-burnt areas and tree re-growth after forest fires in Pakistan and Australia. Subsequently, we described these methods in chapters 3, 4 and 5, and they form the main research work of this dissertation as presented below: The spatio-temporal changes in burnt area were identified using Landsat satellite data and modeling was used to predict the fire ignition and size distribution daily and yearly. Forest fire ignition models are effective ways to predict and classify drivers for ignition probability across broad areas. Various methods were used for the modelling of ignition-distribution, and also performance of the various models was compared in this study. The work is concentrated on burnt areas using Landsat data and to classify forest fire severity with different parameters (climatic, vegetation, topography and human activities). In addition to these four variables, the extent of the burned areas was measured. It is extremely unfortunate, considering that model distribution models of conceptually related species show significant variations between models. The aim was therefore to compare the predictive output and significance of predictable ignition and spatial patterns of three inflammation-distribution model types; one parametric, a statistical model and two machine-learning algorithms: Maximum entropy (Maxent) and Random Forests (RF). Maximum Entropy (Maxent) modelling and Random Forest (RF) machine learning methods were used to evaluate and predict the probability and spatial diffusion pattern of forest fire in the Margalla Hills, Islamabad. We parameterized the models for Margalla Hills, the National Park, Islamabad, Pakistan using 30 years of ignition, socio-economic, and environmental data. To analyze the model output (for modelling process), those validation data sets were used, which were not used in the training process of the model. By applying the most common threshold-independent approach of the receiver operating characteristic (ROC) curve, the projecting output of the model was evaluated. On the vertical axis, all combinations of sensitivities were plotted, and on the horizontal axis, the proportions of false negatives (1-specificity) were plotted. The area under the receiver operating characteristic curve (AUC) was used as a quantitative efficiency indicator. A value of AUC = 1 indicates a perfect estimate, while an AUC <0.5 indicates a bad result. The grading of model results based on the AUC metric was as follows: 90-100% (excellent), 80-90% (very good), 70-80% (good), 60-70% (moderate) and 50-60% (poor). The best predictors of forest fire sites in all models were human population and development variables (although variable rankings were slightly differentiated), as well as elevation. However, despite similar model performance and variables, the map of ignition probabilities generated by Maxent was markedly different from those of the two other models (chapter 3). Furthermore, this study help us to identify the burnt and unburnt area in Margalla Hills Islamabad. In addition to Machine-learning algorithms were used with SAR (Sentinel-1) and Optical (Sentinel-2) for the identification of burnt and unburnt area. Optical and SAR data with ML algorithms were used for the identification of burnt and unburnt scars in the southeastern Australia in 2019-2020 and in Margalla Hills, Islamabad, Pakistan in 2019 and 2020. The backscatter coefficient was also utilized to extract texture measurements from statistics of local based, using a grey level co-occurrence matrix (GLCM). This was due to its sensitivity to the detection of burnt and unburnt scars of textural variation. While the differential Normalized Burnt Ratio (dNBR) was used for Sentinel-2 optical remote sensing to assess the extent of the burnt intensity levels for both regions present in the current analysis. The study was investigated by performing a contextual classifier Support Vector Machine and Markov Random Field classifier (SVM-MRF). This is due to its ability to integrate spectral information and spatial context through the optimal smoothing parameter without degrading image quality. Sentinel-2 scenes were used as reference data to construct the training and test set datasets, which consisted of burned and unburned pixels. The experimental results revealed a strong correlation between the two established classes' spectral and polarimetric sensitivity after classification. These results demonstrated that optical and SAR data have complementary characteristics and can be integrated to provide more information about models and methods. After classification, both forms of spectral sensitivity and Polarimetric sensitivity for the two groups were established, and the experimental results revealed that there was a strong relationship between them. The kappa coefficient and f-score calculation were used to assess the algorithm's performance. In addition to this, the result was also compared to the textural variation of the defined classes by use of GLCM statistical measure. The 𝐻-𝛼 plane entropy decomposition assisted in classifying the object based on their physical properties. After the burn, the entropy and alpha values decreased and formed a pattern. The main statistical features that showed strong distinction of burnt and unburnt scars using backscatter strength were entropy, homogeneity and contrast according to the sensitivity analysis to the GLCM features. After key parameters like the number of quantization levels, window size, pixel pair sampling distance (which was one), and orientation were optimized, this was the result. Sentinel 1's ability to distinguish between burnt and unburned scars was strongly influenced by local incidence angle, acquisition geometry, and environmental factors. In hilly areas, low incidence angles demonstrated better burnt/unburnt area discrimination than high incidence angles. In addition, topography played a large role, as areas facing slopes in hilly areas demonstrated greater discrimination between unburned and burned areas than areas facing back slopes. This led to the conclusion that the severity of the fire and its effect on vegetation structure have a significant impact on the sensitivity of the SAR sensor when analyzing changes in forest structure following a bushfire. In such cases, optical data can also be used as a replacement because it showed high spectral sensitivity to changes in Pakistan fires, regardless of their strength. Nonetheless, the findings in both areas support the use of satellite SAR and optical sensors in forestry applications, and their sensitivity is highly dependent on vegetation structure, the geographical nature of the study field, and the intensity of the burn (chapter 4). Moreover, in order to evaluate the backscatter intensity the Sentinel, 1C-band dual polarized data was investigated to determine the magnitude of the post fire tree survival in forest cover. In this case, Post-fire tree survival in areas affected by high fire impacts was inferred by assessing the recovery rates of the backscatter coefficient. This research evaluated the application of a multi temporal Radar Burn Ratio (RBR) computed using Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery to quantify the effects of prescribed burns in the forests of southeastern Australia. SAR sensor have collected the C band datasets and specifically used those in linking changes in the coefficient of radar backscatter were related to intensity of prescribed burns. Two modelling approaches based on pre and post fire ratios were applied to evaluate prescribed burn impacts. In areas that experienced intense fires, tree survival post-fire was inferred by evaluating the recovery rates of the backscatter coefficient, one year after the fire. The effects of prescribed burns were documented with an overall accuracy of 82% using cross-polarized backscatter (AV) SAR data under dry conditions. The Normalized Difference Backscatter Intensity (NDBI) and the VV polarization illustrate the certain ability to detect burned areas under wet conditions. Findings from this research indicate that C- band SAR backscatter coefficient has the potential to access the effectiveness of prescribed burns in Croajingolong National Park through its sensitivity to conversion in vegetation structure. Moreover, the short post-fire pattern of the backscatter coefficient in areas impacted by high-fire impacts could deduce tree survival (unlike the mortality) and recovery into the pre-fire state. In case study of Pakistan, this study has the objective to evaluate areas burned in forest fires and prescribed fires using Optical (Sentinel-2) imagery and SAR (Sentinel-1) data. Specifically, the objectives are to: i) assess if forest fires and prescribed fires bring a significant difference in backscatter signal of Sentinel 1, ii) evaluate if forest fires have similar fire severity when compared to prescribed fires. To assess these issues, the study was conducted in Margalla Hills, Islamabad, using Sentinel-2 data to create a multi-temporal analysis. For the SAR response to fire events, Sentinel-1 backscatter values were used, and 34 variables were tested in order to see which ones behave more similarly to the spectral indices. For the comparison between prescribed fires and forest fires, the analyses was conducted using Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI). Using visual interpretation to analyses the SAR response, the Normalized Signal Ratio in percentile 95 (NSR p_95) seems to work properly for areas covered with grass and small bushes, but it also seems to work best when the fire severity in the area is greater. 95% of the plots analysed by NSR p_95 were considered as a good response to fires, when compared to spectral indices. The findings of the study raise some managerial implications two of which are worth to be addressed here. One, NSR percentile 95 seems to work properly for areas of grasses and small bushes when assessing burn areas, but it also seems to work best when the fire severity in the area is greater. It is worth noting that, the NSR percentile 95 was initially considered as correctly responding to fire events only 48% of the times where the official fire data from CDA was used as support, but after comparing the SAR values to NBR values, the percentage of “right” responses increased significantly – to 95%. The Sentinel 1 and Sentinel 2 data, provide open data in the two realms of SAR and optical. For instance, in their study used Sentinel-1 data to investigate if such sensor can detect burned areas in vegetated regions, and concluded that the VH polarization effectively responded to the fire occurrences, decreasing its value after the fire event. In addition, the SAR sensor was tested to map the areas likely to be burned using the unclear-burned area-mapping algorithm that integrates the spectral indices into a region-growing algorithm. The results indicate that the use of the SAR system in the mapping of burnt and un-burnt scars delivers consistent estimation of forest fires relative to reflectance-based indices provided by the optical dataset. To sum up, in my opinion this thesis shows insight into optical and radar satellites that provide us better information related with pre and post fire that were poorly described in previous studies. In addition to this, we adopted different Machine learning approaches for the performance assessment of passive (optical) and active (SAR) satellites data for forest fire monitoring, improvement of probability fire ignition modelling, burnt, unburnt scars and post fire tree growth in Pakistan and Australia. Moreover, SAR and optical sensor were used for the identification and mapping of burnt, un-burnt scars and prescribed fire. Finally yet importantly, SAR provided us more information than optical data. It seems likely; the technical solution of this study may be useful for decision-makers in forest management and fire control, not only in Pakistan and Australia, but also in other areas where prescribed burning is commonly applied as a land management tool.
... Accordingly, the fire season occurs between May and July, when the temperatures are warmest and before the beginning of the monsoonal rains [4,5,13,33]. Lightning and the hot and dry weather during the summer months combined with the dry conditions in the autumnal months of September and October after the monsoon season are the natural and primary causes for the ignition and spread of wildfires, while anthropogenic causes coming in second place in reports of past fire incidents [34][35][36]. ...
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The extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography, and in those areas the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015–2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, boxplots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the regeneration time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A comparison of the response of 34 SAR variables with official data on wildfires and prescribed fires from the Capital Development Authority revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) was found to be the best variable to detect fire events, although only 50% of the fires were correctly detected. Nonetheless, when the occurrence of fire events according to the SAR variable NSR p_95 was compared to that from the two spectral indices, the SAR variable was found to correctly identify 95% of fire events. The SAR variable NSR p_95 is thus a suitable alternative to spectral indices to monitor the progress of wildfires and assess their severity when there are limitations to the use of optical images due to cloud coverage or smoke, for instance.
... Incidents of fire arise because of two primary causes, i.e. normal and anthropogenic behaviour. Rock weathering, lightening and hot environment are the normal means of forest fire rising and spreading in the area, while human presence and disturbance in the woodland region and cause of woodland vegetation burning fall in second place based on reports of fire incidents (Brooks and Lusk 2008;Iqbal et al. 2013;Collen et al. 2015). ...
Article
2021) Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla ABSTRACT The objective of this study is to adopt a methodology for analysing spatial patterns of danger of forest fire at Margalla Hills, Islamabad, Pakistan. The work is concentrated on burnt areas using Landsat data and to classify forest fire severity with different parameters (climatic, vegetation, topography and human activities). In addition to these four variables, the extent of the burned areas was measured. Statistical analysis at each fire scene was used to measure the effect on the variables. To calculate the fire severity ratio correlated to each variable, logistic and stepwise regressions were used. The results showed that the burned areas have increased at a rate of 25.848 ha/day (R 2 ¼ 0.98) if the number of total days since the start of fire has increased. As a result, forest density, distance to roads, average quarterly maximum temperature and average quarterly mean wind speed were highly correlated with the fire severity. Only average quarterly maximum temperature and forest density affected the size of the burnt areas. Prediction maps indicate that 53% of forests are in the very low severity level (0.25-0.45), 25% in the low level (0.45-0.65) and 22% in high and very high levels (>0.65). ARTICLE HISTORY
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Landsat, first placed in orbit in 1972, established the U.S. as the world leader in land remote sensing. The Landsat system has contributed significantly to the understanding of the Earth’s environment, spawned revolutionary uses of space-based data by the commercial value-added industry, and encouraged a new generation of commercial satellites that provide regional, high-resolution spatial images. This PE&RS Special Issue provides an update to the 1997 25th Landsat anniversary issue, particularly focused on the contribution of Landsat-7 to the 34+ year history of the Landsat mission. In this overview paper, we place the Landsat-7 system in context and show how mission operations have changed over time, increasingly exploiting the global monitoring capabilities of the Landsat observatory. Although considerable progress was made during the Landsat-7 era, there is much yet to learn about the historical record of Landsat global coverage: a truly valuable national treasure. The time to do so is now, as the memories of the early days of this historic program are fading as we speak.
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Lagos, like most other coastal cities is associated with many of the consequences of such ecological relationships, which include physical alterations and destruction of coastal habitats, flooding, erosion, pollution and continued threats from rising sea levels. Thus, in order to ensure sustainable coastal area management, techniques are required that provide cost effective means for mapping and monitoring landcover change and impacts. In this study, the aim was to evaluate the attendant socio-economic and environmental implications of the changing pattern of landcover change associated with the Lagos coastal zone. The observed (1986-2002) and predicted (2002-2027) rapid and continuing landcover change in the Lagos coastal area have multifarious implications on the residents and inhabitants of the area and on the entire Lagos residents in general; which is a consequence of the multiple impacts (positive and negative) that affect the ability of biological systems to support human needs. Some of the positive impacts of landuse/cover change include the continued increase in food and fibre production, resource use efficiency, wealth, livelihood security, welfare and human well-being. However, the undesirable and negative impacts of landcover change include massive alterations o f biogeochemical cycles (e.g. nitrogen, carbon and water), ecosystem processes, earth-atmosphere interactions, loss of biodiversity and soil degradation at different spatial and temporal scales. For instance, the expansion of the developed landcover into the swamp landcover type would have destructive consequence on the ecological biodiversity of the area and an attendant reduction in the livelihood of those that depend on these vegetal resources.
Article
The study was carried out on the growth, distribution, classification and correlation of herbaceous vegetation edaphic factors in Margalla Hills National Park, Islamabad. Sampling of vegetation and soil was performed using random sampling method. A total of 52 herbaceous plant species from 26 families were recorded in 30 quadrats. The study aimed to classify and identify plant species and to understand the soil factors playing role in community composition. TWINSPAN was used to identify distinct plant communities, which resulted in the recognition of four vegetation groups. Malvastrum coromandelianum and Cicer arietinum community was present along the agricultural crop fields, Cynodon dactylon and Cerastium fontanum community occupied the humid stands, Micromeria biflora and Grewia tenax community was present in a majority of areas. Lepidium pinnatifidum and Coronopus didymus community was grown along the road in the study area. DCA was used to determine the dominant communities in the study area. Understanding vegetation distribution in this area can help for management, reclamation, and development of Margalla Hills National park ecosystems.
Article
We used three Landsat images together with socio‐economic data in a post‐classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Nairobi city. Land use/cover statistics, extracted from Landsat Multi‐spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) images for 1976, 1988 and 2000 respectively, revealed that the built‐up area has expanded by about 47 km. The road network has influenced the spatial patterns and structure of urban development, so that the expansion of the built‐up areas has assumed an accretive as well as linear growth along the major roads. The urban expansion has been accompanied by loss of forests and urban sprawl. Integration of demographic and socio‐economic data with land use/cover change revealed that economic growth and proximity to transportation routes have been the major factors promoting urban expansion. Topography, geology and soils were also analysed as possible factors influencing expansion. The integration of remote sensing and Geographical Information System (GIS) was found to be effective in monitoring land use/cover changes and providing valuable information necessary for planning and research. A better understanding of the spatial and temporal dynamics of the city's growth, provided by this study, forms a basis for better planning and effective spatial organization of urban activities for future development of Nairobi city.
Article
In remote sensing, principal components analysis is usually performed using unstandardized variables. However, the use of standardized variables yields significantly different results. In this paper principal components of two LANDSAT MSS subscenes were separately calculated using both methods. The result indicate substantial improvement in signal-to-noise ratio and image enhancement by using standardized variables in the principal components analysis.
Article
This paper investigates the incorporation of ancillary spatial data to improve the accuracy and specificity of a land-use classification from Landsat Thematic Mapper (TM) imagery for nonpoint source pollution modeling in a small urban area - the city of Beaver Dam, Wisconsin. A post-classification model was developed to identify and correct areas of confusion in the Landsat TM classification. Zoning and housing density data were used to modify the initial classification. Land-use classification accuracy improved and the number of identifiable classes increased. Additionally, confusion between classes that were commonly misclassified (for example, commercial and industrial areas) was reduced. -from Authors