Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan

*Amjad ur Rahman, Esra Gürbüz, Semih Ekercin and Shujaul Mulk Khan*

#### **Abstract**

The current study deals with the mapping and evaluation of forest and land use cover changes in the western Himalayas, Pakistan. These forest types include i) Moist temperate forests ii) Mixed coniferous forests and iii) Sub-tropical broad leaved forests. Moist temperate forest mostly consists of evergreen conifers with some of oaks and deciduous trees. Subtropical pine forest are mostly dominated by *Pinus roxburghii*. These forest type are mostly mixed by *Pinus roxburghii* and other coniferous species like *Pinus wallichiana* at the upper ranges in Dewal, Angoori, Nambal, Aucha and Khanitak etc. The broad-leaved subtropical forests are recorded on the hills and in the lower slopes of Himalaya near Islamabad and Rawalpindi. The high quantity of vegetation index were observed in winter season as compared to summer. The Landsat satellite images of years 1988, 1998, 2008 and 2018 were classified into land-cover units. Vegetation land decreased in the total area whereas the bare land class increased in the total. Water class further reduced and the builtup class increased up in the Murree area, Pakistan.

**Keywords:** Murree mountains, Remote sensing, Landsat images, NDVI

#### **1. Introduction**

In mountain ecosystems, vegetation serves the very first trophic level. Vegetation is the plant composition of any given area which possesses characteristic physiognomy including various taxonomic groups and present in a particular microclimatic space [1, 2]. Plant communities are the characteristic assemblage of plant species which is determined by the interaction of vegetation with other biotic and abiotic component and can easily be differentiated from each other (RIFFAT NASEEM [3]; RIFFAT N [4]). A plant community is a group of plants that have collective relationships with each other and their immediate environment [5]. The climate, topography and soil affect the characteristics of each plant community. The course or form of the plant community or types of vegetation is also shaped by biotic factors, especially human influences [6]. It forms a reasonably uniform layer that is distinct from neighboring patches of various types of vegetation. The nature and development of plant communities represents the conditions in which they are developed [7]. Various aspects of vegetation studies also contribute to the conservation and

management of plant diversity. These studies also evaluate of the ecological impact and uses of vegetation and analyses of potential future changes [8].

The unique species aggregation of an area reflects the effects of environment on vegetation. Vegetation complex fluctuates in correspondence with the environmental fluctuations, which might be a seasonal or long term in nature [9]. Vegetation of a region strongly depend on climatic, soil and variation in disturbance levels that itself affects other factors as well [10]. Vegetation thus provides valuable information about the health of an ecosystem. The concept of vegetation can historically be demonstrated as a means of organizing plant assemblies at various spatial scales. The composition of plants has changed, mostly over time, and human activities have become increasingly concerned with the esthetic and socio-economic values of natural resources [11, 12]. The information can be used to manage an ecosystem, habitat and productivity of the area. Different environmental variables have different effects on the vegetation but all the environmental variables have a cumulative dynamic effect on plant species composition of an area [13]. Phytogeographical and phytosociological research all over the globe try to classify vegetation into plant communities based on composition, development and co-occurrence of species [14] which is important in ecological research to explore areas for the first time [15]. It deals with the species composition of plant communities, their evolution and the relationships between the species present. Gradient analyses are its complementary tools to understand functioning and description of vegetation [16, 17].

#### **2. Vegetation dynamics**

Vegetation dynamics represent the net effects of several variables, including climate, biotic interactions, abiotic environment and the level and history of disturbances. There is an emerging trend in ecological research to know how these variables interact and influence the coexistence and productivity of species over a time and space in an ecosystem. Vegetation Dynamics are complex phenomena in many ways and need to be accessed via varied angles. Such dynamics are functions of the disturbance regimes in a particular spatiotemporal range. The higher biological diversity of specific sort of vegetation over the other is sometimes supported by the natural disorders. Forest management strategies thus need the knowledge of natural disturbances within a given region [18].

Several studies have shown that natural resources in northern Pakistan, especially forest resources, are constantly decreasing because of growing human population, increase in demand of fuel and timber wood, and expansion of land for crops. Careless in collection of medicinal plants, over grazing, and mismanagement are other causes of reduction in forests. Furthermore, the actual state of forest cover in the country is controversial and been assessed quite longer time ago [19–23].

#### **2.1 Himalayan perspective of the vegetation dynamics**

Himalaya is derived from the Sanskrit word which means "abode of snow" comprising a wide-ranging consistent arch about 2600 kilometers along northern border of subcontinent from the Indus river of Pakistan [24]. The Himalaya contains the highest mountains in the world with the highest ecological amplitude [25]. The Himalaya include the most inexperienced habitat on earth that cherish varying biodiversity of forest types due to critical climatic changes, topographical and soil composition from the foothills to alpine mountaintops. The Himalayan flora is diverse and varies in the southeast from tropical evergreen forest species

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

to thorn steppe and alpine species in the northwestern regions [26]. The lesser elevation range (901-1501 m) of Himalayas were occupy by subtropical broad leaved and mixed pine forests, leading by *Pinus roxburghii*, *Dodoneaviscosa, Olea cuspidata*, *Pinus walichiana*, *Punicaflorida*and *Acacia nilotica* species etc. [27]. The moist temperate forests and cool moist temperate forests were prevailed above the lesser elevation range with *Abies pindrow*, *Pinus wallichiana*, *Cedrus deodara*, *Asculus indica* and *Quercus dilatata* [28].

Pakistani Himalaya is gifted with richness of plant biodiversity. The north western Himalayan zone is one of the 18 hotspots of the biosphere. Enormous geological, geographical and climatic variations in altitude, topography, temperature, precipitation, soil condition bring subsequent diversity in forestry, horticulture and wildlife of the region. These mountain ranges consist of a series of chains that run roughly parallel to each other for long distances and cover areas including a chain of valleys, and glaciers. Few high altitudinal regions of the Himalayan forests are comparatively protected due to their remoteness and less population densities. The lower subtropical and moist temperate forests are the most severe victims of anthropogenic stress resulting in massive forest losses. Three-quarters of the western Himalayan forest cover is reported to have been disappeared in the last century [29]. Prabhakar et al. [30] projected 60% forest deterioration in the states of Garhwal and Uttarkhand in Indian Himalayan. A rapid forest cover decline from 57–23% was recorded in Nepal from 1950 to 1980 with all of Nepal's subtropical forests either severely degraded or completely lost [31]. Due to heavy deforestation in Pakistan merely 4.8% of land remains covered with forests with an enduring deforestation annual rate of more than 3% [32]. Large scale logging activities in Yunnan of Chinese Himalayan province caused 20% forest cover deterioration from 1960s to 1990s [33]. Pakistan vanished 25% of forest cover in only 15 years during 1990 and 2005 [34]. Forest loss of a total of 23% in western Himalayas and 8% in Eastern Himalaya has been assessed by using GIS and remote sensing methods in last three decades [35]. The condition in Bhutan is quite different due to strict implementation of forest conservation plans, where about 60% of the country area remains forest covered, even though restricted exploitation remains [36].

#### **2.2 Vegetation and climate change**

The dynamics of vegetation are considered as a significant indicator for the regulation of the terrestrial equilibrium of carbon and climate change. This challenge is significant for the climate change assessment. Even though correlations between vegetation dynamics, temperature and precipitation have been widely studied, the correlated issues are linked to the relations between vegetation dynamics and other climatic variables. Monitoring the long term change of vegetation growth and exploring its relations with climate change is relevant for the global change study [37, 38].

Vegetation dynamics are very vulnerable to climate change in particular [39–41]. A prevalent research area has been the use of remotely sensed data to dynamically analyze the interannual variations of long-term sequence vegetation [42, 43]. Several researchers have used various vegetation indices and models to analyze and assess trends in vegetation change [44–46].

#### **3. Quantitative ecological assessment of vegetation**

Visual estimations have been used in vegetation assessment despite of more recent development of reliable numerical measurement techniques for the quantification of

vegetation attributes. The evaluation of data by counting, measuring or even other ways of direct measuring is more comparable to approximate eye approximation. According to several ecologists, this does not provide a systematic way of evaluating vegetation parameters. Various methods have been suggested for this purpose in order to optimize the data collection on fields, supported via mathematical and statistical procedures in order to bring an accurate representation of vegetation [47].

The use of multivariate methodologies in the investigation of vegetation data has many advantages. Ecologists get support from computer based multivariate systematic statistic softwares to define structure in data sets and to assess effects on complete group of species. Nowadays computer technology evolving is fast and cheap [48]. Various softwares designed for vegetation sciences can be used for comprehensive models, interpretation and approaches of descriptive statistics of plant communities. Vegetation science approaches include sampling, classification of vegetation, gradients analysis and investigation of association between species distribution and their atmosphere.

Recently several ecologists have been working to determine the underlying mechanism of vegetation composition in the entire vegetation complexes. The use of multivariate statistical methods such as Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA), Cluster Analysis and other statistical techniques have advanced ecological techniques [49, 50]. Cluster analysis is a technique of classification used to characterize and combine ecological communities into associations or clusters. DCA utilizes an Eigen vector of indirect gradient method focusing on investigation of plants distribution [51–53]. As only plant plants data are needed for DCA study, it presents the results without interference. CCA, on the other hand, is a direct gradient, analytical method in which environmental factors regulate the distribution of plants. CCA is being used to establish association between plants and environmental factors [54–57]. Regression analysis is combined with either reciprocal averaging or correspondence analysis by the CCA method [58].

Vegetation assessment has been giving strong bases for improvement of ecological science for several decades. Plants at individual or at community levels in response to its environment can be measured by means of quantitative and qualitative ecology (Phytosociology). Phytosociology can also be used to explore plant community services over both quantifiable and qualitative methods from plants to community level, as it offers understanding of species diversity and significance values [59, 60]. It is a developed field that explains the diversity of the plant communities and relationship with the environment [61]. The distribution of individuals in a community of the same or dissimilar species is a function of micro environmental changes, biotic interactions, and time. Therefore, understanding of vegetation can be helpful in assessing plant assembly of species in community in a specific manner [12, 62, 63]. In ecology, natural resource management and ecosystem protection, knowledge of plant species is a crucial requirement. This understanding is essential for the evaluation of rare plant species and the development of management policies to protect and minimize habitat fragmentation [16, 64].

The investigation of plant diversity is a key concern to ecologists as it offers the foundation for global conservations policy [65]. Phytosociological procedures permit environmentalists to compute plant diversity, abundance and richness of plant species in ecosystems. These methods not only assist to comprehend almost conservation but moreover measure as indicators of specific habitat forms. In addition, important value indices (IVI) can be calculated from datasets that not merely provide an understanding of the heterogeneity of floral phenomena, but can also be used to provide an indication of plant conservation needs [66, 67]. Furthermore frequency, fidelity and

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

constancy investigation helps to recognize the threatened plants and those habitats requiring protection [68, 69].

The use of several indicators and indices for better understanding in relation to anthropological activities are recommended [70, 71]. Single indicator cannot specify all aspects of biodiversity. On the basis of broad vegetation explanation and statistical investigation, indicator plants were recognized. Khan et al. [72] recognized indicator species based on the Indicator Species Analysis (ISA) in western Himalayas. At least one significant indicator was nominated from each of herb, shrub and tree layers in every community. Further vegetation studies along ecological gradients in mountains ecosystem have just matched the indices of diversity among communities. All species were treated correspondingly without seeing their ecological location and their importance in those specific ecosystems [73–75]. Plant species with greater fidelity rates were reflected to need the supreme conservation significance. Those type of species having limited distribution and perhaps patchy habitats at maximum danger [69, 76, 77].

#### **4. Biotic and abiotic interaction and vegetation**

Biotic interaction in the vegetation is the most significant factor for many plants affecting their surrounding plant species in environment. In ecology one of the most important debate focuses on the issue of the mechanisms by which plants interact with one another. Interactions between plants - plants vary from positive (facilitation) to negative (competition) impact on neighboring plants [78]. Plants which germinate on the floor of the forest are climbers. Climbers grow by winding around, anchoring or adhering to other plants to achieve great stature for at least part of their life, or when the forest closes up around them [79].

The analysis of plant species – abiotic environmental variables is considered as an important subject in the ecological and environmental sciences. This type of interrelationship between environmental variables and forests is essential to any assessment [80, 81]. Floristic composition and its relation to environmental variables has become a recent subject of research. Multiple studies have also shown that environmental variables are mostly correlated with the vegetation patterns and distribution, comprising local topographic variables (elevation, slope, aspect), soil factors (physical and chemical properties) and anthropogenic factors [81–86]. Among abiotic variables, soil factors are the most important features affecting plant diversity and abundance in an area [87]. In general, soil factors comprising of total nitrogen, organic carbon, and clay etc. primarily regulate the distribution of vegetation patterns. Main factors affecting plant species abundance, growth are the soil nutrients and physic-chemical soil properties [86, 88–90]. Each species needs a particular nutrient contents and chemical composition to develop. The composition of these variables defines the fundamental habitat of an organism, described as the variety of conditions and resources under which individuals of a species can survive. Physiological tolerances to abiotic variables decide the boundaries of a basic habitat. Thus, abiotic variables have been found to be the strong determining features of certain plant species' development. Therefore, the distribution of plants within their environment is determined by the combination of abiotic variables, some of which are more significant than the others. On a local scale, what particular abiotic variables determine the fundamental habitat of a species? And what variables lead to increased success in one area in some habitats, and not in others? [91, 92]. Various approaches are used to determine such a complex relationship.

#### **4.1 Geo-informatics and vegetation evaluation**

Data collection to produce logical information about the dynamics of an ecosystem had been expensive and time consuming process. Consequently, our knowledge of globally important ecosystems like Western Himalayas, especially those which are found in the developing countries like Pakistan have been insufficient. However, with the invention and applications of satellite remote sensing techniques, these areas are getting international attention with detailed studies towards monitoring of biodiversity and ecosystem conservation [93]. There is a need for speedy and innovative technologies for ecosystem management, inventories and valuation of biodiversity, environmental monitoring and species habitat suitability investigation. These technologies should be based on physical factors of the ecosystem and socio-economic situations. Habitat mapping provides knowledge about quality and quantity of vegetation cover, the physical set up and anthropological interactions. Technological development in the area of remote sensing and GIS holds the promise to collect and integrate different levels of information [94].

Remote sensing data provide evidence with respect to location and extent of available areas and its spatial dissemination for execution of several problems. In the recent time relations between ecology and remote sensing have been considerably increased because of developments in imaging spectroscopy [95, 96]. GIS is associated with a powerful reference base or geographical locations including maps of vegetation, topography, soil, bird migration, hydrology and distribution of other wildlife. Locating various features related with attributes could allow various data sets to be combined and compared. It may also be analyzed in a particular data-base to create new relationship between environmental properties and the diverse biota. GIS, therefore, is an effective and powerful means of tool to communicate a wide range of data within the shortest possible time scale [97]. GPS is a ground based satellite and radio navigation system that facilitates the user to fix the accurate positions on the surface of earth. Therefore GPS and remote sensing have given rise to the beginning of more accurate and geographically referenced data sets for improved analysis [98, 99].

Knowledge about the distribution and status of species are important for wildlife research and conservation policies. Remote sensing and GIS are progressively used in monitoring flora and fauna habitats. In order to discover potential habitats for species such as the hamadryas baboons (Papiohamadras) in Eritrea, for example knowledge about the distribution of the main habitat features such as food sources, water supply, sharp cliffs and elevation of the area were digitized from topographical maps and remote data sets. It is demonstrated that locations with a mixture of these features are deliberated to be potential habitat for the species concerned [100]. Therefore, remote sensing and GIS are broadly used to discover potential habitats, digitize the information and then mapping the appropriate habitats. There has been a quick rise in the usage of remotely sensed evidence for biodiversity assessment, land management, wildlife ecology, aquatic ecology as well as observing the effects of greenhouse gases and additional environmental problems [93].

Satellite imagery is a valuable basis for land use land cover information and urban land cover has been recognized and diagramed by remote sensed data with a reasonable spatial resolution [101, 102]. In the current years there has been a growing understanding of the impacts of geographical features in ecosystems. In specific important factors like spatial and scale configurations have become progressively significant in a huge range of ecological studies [103]. Remote Sensing currently provides ecologists and other scientists with regular information on the earth and its atmosphere at the regional to global scale. GIS provides resources to collect evaluate and visualizes spatial data containing those resulting from remote *Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

sensing altogether with related innovations in computational specialist tools and facilities [104, 105].

Spectral resolution of the Remote Sensing method has high potential for monitoring land use land cover behavior, natural resources environment and hazards of land degradation in forest areas in Pakistan. Remote sensing and GIS can subsidize to observing land use land cover in comprehensive means. Remote sensing has regularly been used to develop land covers evidence. Variations in land use and land cover are main variables affecting environmental system. Land use land cover types fluctuate significantly in their bio-geochemical cycling and hence information of their distribution is imperative in many ecological modeling studies. Land cover changes have major effects on matters fluctuating from climate change to biodiversity management. Given that the remotely sensed response is principally a function of land cover type there has been substantial interest in utilizing remotely sensed data sets as a source of evidence on land use land cover [106]. GIS and RS role is very significant to evaluate the spatial and temporal forest and urban land use classes. Zafar et al. [107] assessed land use variations by using Remote sensing datasets for zonation organization of Margalla Hills National Park, Islamabad Pakistan on the base of diverse environmental variables. Wardlow et al. [108] utilized time series remotely sensed data for judgment of crops and related land cover types. Wheat harvest was projected based on the analysis and interpretation of the images. Ashraf et al. [109] investigated satellite imageries datasets of drought and postdrought (2001 to 2006) phases in order to evaluate variations in vegetation cover and land uses over hybrid (digital and visual) explanation method.

Progressively organizations involved in forest conservation and management are utilizing this expertise to capture and analyze spatial occurrences. In conservation biology the emphasis has currently moved from individual species to entire ecosystems. GIS and Remote sensing methods could be utilized for inventorying, assessing and monitoring terrestrial biodiversity at local landscape and community ecosystem ranks [110]. Gap examination is a GIS based technique to identify breaches in the safeguard network [111]. In a gap investigation of Western Ghats, India, Ramesh et al. [112] establish that numerous regions of high biodiversity were omitted from the highest stages of protection. Recent developments in GIS and Remote sensing technologies have made it promising to quantify forest biodiversity from satellite imageries.

#### **5. Vegetation of Pakistan and study area**

The vegetation types of Pakistan can broadly be divided into; a) Tropical:- Tropical dry deciduous forests, Littoral and Swap forests, Tropical thorn forests, b) Sub-tropical:- Sub-tropical pine forests and Sub-tropical broad-leaved evergreen forests, c) Temperate:- Himalayan moist temperate forests and Dry temperate forests, and d) Alpine:- Alpine scrub and Sub-alpine forests [113].

Administratively study area is divided in six parts; 4 sub-divisions (Sambli, Ghora Gali, Sehr Bagla, and Lower Topa) and 2 ranges (Ban and Municipal range) shown in **Table 1**. Murree forest division is approximately 44% of the Murree Tehsil which contains of almost 19,135–20127 hectares of forested land (State owned).

Murree is the most famous hill station in Pakistan. Murree located at 33–34° north latitudes and 73.30° east longitudes and lies 50 kilometers northeast from Islamabad, Pakistani capital at an easy elevation ranges changing from 500 to 2300 m in the Himalayan foothills.

Murree is a mountainous region founding portion of the outer Himalayas in western side Pakistan. It comprises of four progressively growing foothills (**Figure 1**).

#### **Table 1.**

*Murree area subdivisions/ranges.*

**Figure 1.** *Murree brewery remains in the Murree.*

On the highest among these is the Murree city itself situated at altitude of 2290- 2300 m. Other peaks contain Patriata, Gharial and Kuldana. It is delimited in the east by River Jhelum, Haripur and Abbottabad districts of Khyber Pakhtunkhwa (KPK) to the West and North, in southwest Islamabad Capital Territory and in the South KotliSattian town of Rawalpindi district. Murree municipality was built in line with the European municipalities with Churches in the center and main The Mall road running along with commercial areas and organizational offices around this. The Mall road was and is still the center of charm. Non-Europeans were not permissible to entrance to Mall road till the independence in 1947.

#### **6. Murree Forest division (MFD)**

Murree Forest Division is part of Ecoregion of Western Himalayas which is familiar as among the Ecoregions of the World (referred to as G200/Global 200) based on biodiversity and ecological significance. Murree Forest Division is a famous hill station and very well-known tourist hotspot in Western Himalaya of Pakistan. It is situated along Islamabad - Kohala highway, 30 kilometers northeast of Islamabad. Murree is one of the large tehsil of District Rawalpindi, Punjab. Geographically MFD is lies and centered at 33°52′26.34″ north and 73°23′42.21″ east (**Figure 2**). MFD is located in diverse ecological zones from Himalayan moist

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

**Figure 2.** *Administrative division of Murree.*

temperate to Broad-leaved deciduous forest at lower elevations [114]. İts elevation ranges 500-2380masl (1700-7800 ft). Murree hills came in to existence by the collision of Indian plate and Eurasian platen by a rapid raise in early Ecocene era [115]. The area gives secenic view and is important in having compact forest at higher elevations which typically includes Cedrus, blue and chir pine forests.

#### **7. Geology, geography and climate**

The Himalayan range which is 2500 km long was formed during Eocene period by the collision of the Eurasian plate with Indian plate the along a junction region about 20 million year back constructing quickly elevating zones [34, 116]. Murree region comprises of brittle rocks with hard gray reddish sandstones interbedded with soft red calcareous shales and alluvial deposits belongings to Shiwalik and Sirmar series of sub-Himalayas structure [116–119]. This region fall in tertiary and quaternary sediments dominate the zone with extensive rock formation of Shiwalik nature [120, 121]. Sedimentary rocks of the Murree area are highly mutilated due to active geological faults and tectonic pressures. These rocks have the uppermost affinity to landslide hazards [116, 122]. The Murree city is built in the European style that is the reason that has Church in middle and marketable regions alongside the main Mall road. At the time of foundation it comprised of only five major regions but with the passage of period there were several territorial variations in Murree area. At present Murree is divided into fifteen union councils (UCs) and cantonment zones. Some of the UCs contain Murree are Angoori, Ghoragali, Charhan, Dewal, Phagwari, Mosyari, Potha Sharif, Nambal, Tret and Sehrbagla etc. Another geographic significance of the Murree is that it links the Punjab plains to the Kohala, Azad Kashmir, and Abbottabad, KPK province.

Climatically the Murree hills are in divided into diverse regions from higher temperate zone to subtropical lowlands. Such a varied climate in a small geographical area to a diverse topography is triggered by the differences in elevation, depth of the snow accumulation amount of snowfall in winter and changing vegetation

etc. The weather has four distinct seasons i.e. summer, winter, spring, and autumn. Usually the climate is cooler at higher elevation and warmer at lowlands with a short autumn and spring seasons. In the western Himalayas higher mountains located at the opening of the hills act as a block to the summer monsoon and bound its dissemination into the upper north western parts of the mountains [123, 124]. Winter normally start in the December and gets considerable snowfall. Murree and its neighboring parts are shielded with thick layer of snow in most of the winter. Temperature frequently drifts round about the freezing point. Summer duration lasts during May to the end of August. The months of June and July are the topmost tourists' season in the Murree.

#### **8. Temperature and rainfall**

Murree region exhibits extensive differences in temperature due to substantial altitudinal and topographic variations. The mean lowest temperature was 4–9°C whereas highest temperature was 27–30°C respectively during the years 1988–2018 (Metrological Department Islamabad, Pakistan). The warmest month of the year was June, 2018 with an average maximum temperature of 30°C. Spring season in Murree area lasts from March to the middle of May. Maximum temperature in this period fluctuates between 12 and 20°C and minimum 4–9°C respectively. Monsoon winds are the leading source of rainfall. The Murree hills receives the highest quantity of precipitation in Pakistan with an average of 1,640 mm- 1,904 mm and nearly 89 mean rainy days per year [117, 125, 126]. On the other hand several parts of the region receive fluctuating quantities of precipitation. The majority of the rainfall is received from July to August during the monsoon.

#### **9. Murree bio-physical environment**

Ecological research on habitat forms has not been performed comprehensively in some of the mountainous regions of Pakistan, particularly in the Himalayan regions. For the first time [113] designated the forest types of Pakistan using the wide-ranging groups: alpine scrub, subalpine forests, Himalayan dry temperate, Himalayan moist temperate, subtropical pine forests, tropical thorn forests, dry sub-tropical forests and marshlands etc. [28] well-defined key habitat forms as; cold desert, alpine vegetation, dry temperate, subalpine forests, moist temperate forests, subtropical forests, subtropical semi evergreen forests, tropical dry deciduous forests, tropical thorn forests and tropical swamplands. All these vegetation forms excluding the swamplands are characterized in the northwestern parts of the Pakistan [28, 113]. The Murree Mountains are placed on the foothills of the Western Himalaya, Pakistan and therefore forms a portion of the internationally acknowledged Western Himalayan floristic province (G200) of western Asiatic Irano-Turanian sub region. Its floristic, climatic, geological, geomorphological and geographical setting makes it among the unique biodiversity hotspots. This offers a particular phyto-geographical importance to the Murree hills and its flora.

#### **10. Urban and rural division**

Most of the urban populations live in the areas of two cantonments and Murree City (**Figure 3**). Permanent urban residents are few and most of the urban parts have private corporations, rest houses of government and summer resorts of elite

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

**Figure 3.** *Urbanization expansions in the Murree City.*

class. Other important commercial institutes are the General Post Office, tailors and millinery and general merchants. Murree Brewery was established in 1860 at GhoraGali to satisfy to the drinking desires of the British in the area. About 88% of the rural population lives in small villages spread over all the top of Murree hills. The village residents have easy convenience to the local primary and secondary schools, clinics and bazaars. But water supply to the families of villages has constantly been a problem. People are migrating mostly for new job opportunities, the lack of other supplies like tap water, gas, roads and to meet the higher education needs of their children. Major portion of these people migrate near the low rental settlements of Rawalpindi and Islamabad.

#### **11. Education and literacy**

As per 1998 census Murree was reported with 69% literacy rate in the age of ten. Murree region is among the well-educated parts in Pakistan and simply exceeds main cities in this respect. Ausia area having education rate of 82.7% in the populations of just 4450 residents is among the maximum literate areas in the country. There is undoubtedly no other rural village of same extent with such extraordinary literacy rate anyplace in Pakistan. In the beginning primary schools were established in the Ausia, Murree, Karor and Tret. There has been one degree college (for girls and boys each) a present in the Murree area. At Phagwari region, one additional girl's college was established. Moreover there are 112 boys and 109 girls primary schools, two boys higher secondary schools at Tanda and Ausia, 16 boys secondary and 6 girls high schools whereas 12 boys middle and fifteen girls middle schools.

Murree too is renowned for its elite academic institutions that have attracted student from all over the region. Lawrence College was founded as Memorial Asylum (Lawrence) around 1860 at GhoraGali for kids of retired or serving British armed forces far from tropical environment of subcontinent. It was portion of four such schools chain which established through British India. Lawrence College is covering a space of more than 150 acres at an elevation of 1950 m and provides education from grade 1 to O and A level and is very famous among elite class in Pakistan (**Figure 4**).

**Figure 4.** *Lawrence College, GhoraGali, Murree.*

**Figure 5.** *Front look of convent of Jesus and marry building.*

Other colleges established in the course of the British for the children of British colonist but nowadays serving the Pakistani aristocracy includes Convent of Jesus and Marry and Saint Dynes (**Figure 5**). Saint Dynese has in the recent times closed its lodging to in order to accommodate the necessities of native population. After independence further schools established contain Army Public School, Cadet College Murree at Pindi Point and Cadet College Lower Topa. It would be discriminating not to discuss Murree Christian School which was unfortunately exposed to terrorist attack during 2002. Murree Christian School situated at Garial near JikaGali assist educational necessities of kids of missionaries who serve in Pakistan. It receives admission from grade 4–12 and is open for kids from Christian family even if they work in other occupations. It is head office of the Murree-Town (sub-division) of Rawalpindi district, Punjab of Pakistani.

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

#### **12. Livelihood**

Livelihood in most of the remote areas in the Murree Mountains is one of the challenges for survival. Typically, people have more than one kind of profession in order to keep sustainable livelihoods. Commonly each family keeps livestock grazing to meet their dairy and poultry requirements and to earn a living out of it as well. The quantities and types of livestock differ from a few to hundreds. Majority of the households keeps cows, buffalos and goats etc. The live stocks provide the livelihoods to the indigenous people for the reason that the rangelands of the Murree region are full of palatable and nutritious grass species [127, 128]. Grasses are harvested and stored which are supposed to be used as dry-fodder which is fed to cattles during the winter seasons. Now majority of the people are switching towards the adjacent cities of Islamabad and Rawalpindi for other professions.

As Murree area is mostly rain fed and hence the agricultural economy contingent to rainfall and to a particular level on water providing by mountains springs and streams. The area in Murree region is cultivated up to round 2000 m asl with fruits and cereals frequently on stepped slopes even though there are also large parts which are uncultivated and have thin soil with slight vegetation. In few parts of the Murree old ways of agriculture are still experienced where the fields are plowed with bullocks. The commonly grown crops in the Murree area are wheat, Millet, maize, Barley, Mustard, Sunflower, Turnip, Pulses, Tomato, Pumpkins, Radish, Cucumber, Lady finger and Potato etc. Fruits trees like Apple, pear, Citrus, Plum, Guava, Apricot, walnut and Peach are grown in the area [129].

Murree's livelihood also depends greatly on tourism during the January to mid-October of tourists' season. Monthly from 20,000 to 25,500 tourists visits these foothills ranges. Similarly each year more than one million tourist visits Murree area and the number is rising by 5% each year throughout the times of political serene [116]. Domestic tourism contributed Rs. 89 billion in 2018, contributed up to 30% of the total domestic travels and tourism expenditures.

#### **13. Importance of the present study**

Vegetation dynamics in the foothills of Western Himalayas have been rarely studied for ecological evaluation and vegetation dynamics. Vegetation diversity in these mountains is under huge anthropogenic pressures in the form of over grazing, poor collection approaches, flawed storing of medicinal plants, uneducated and ignorant people and unmaintainable Government policies [130]. The consistent abandoned harvesting of essential medicinal plants along with augmented habitat degradation and human interventions in their distribution zones has overwhelming effect on normal populations. Numerous essential plants having small ecological place and being constantly exploited are vulnerable to their presence [27]. In this situation basic phytosociological knowledge about vegetation dynamics and distribution is instantly mandatory to develop and launch a conservation plan.

Phytosociological studies disclose that many of the struggles in such disciplines performed individually; focusing on only one approach. The struggles done are not restricted to ecological either quantitative vegetation characteristics such as frequency, density, and cover or only floristic inventories of the vegetation but also utilized geo-informatics (GIS & RS) tools. Therefore in the current research an integrated struggle was made to study, investigate and argue both important sides of vegetation i.e. Ecological multivariate analysis and their association with environmental and anthropogenic variables and GIS & Remote sensing tools for long term historical forest dynamics assessment by land use land cover. This research work was planned to collect baseline information about phytosociology; species composition and distribution pattern, stand population structure, frequency, density, abundance and other essential species as well as community physiognomies in the western Himalayan region.

In the present study, an attempt has been made to examine and evaluate association of vegetation in relation to important environmental factors using multivariate analyses procedures. Very limited quantitative studies have been conducted so far in forests regions of western Himalaya of Pakistan to explain different population structure and forest types. Similarly very few studies have been carried out by using GIS and Remote sensing tools. Therefore this research was intended to describe quantitative description and population structure of diverse Himalayan forests in Murree Mountains, Pakistan. Another important aspect was to analyze the individual characteristics and mapping the forest vegetation into diverse forest types. Data acquired over a combination of these methods make available basic information for conservation planners and biodiversity managers to assess ecosystem services delivered by mountain ecosystem and to articulate sustainable management policies.

## **14. Forest types and vegetation diversity**

MFD consists of three types of forests based on indicator species and environmental factors. These forest types include i) Moist temperate forests ii) Mixed coniferous forests and iii) Sub-tropical broad leaved forests (**Figure 6**).

**Figure 6.** *Forest types in the Murree, Western Himalayas, Pakistan.*

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

**Figure 7.** *Moist temperate forests in Pakistani Western Himalaya.*

#### **14.1 Western Himalayan moist temperate forest**

The Himalayan moist temperate forest mostly consists of evergreen conifers with some of oaks and deciduous trees. Moist Temperate Forest occurs in the altitudinal zone ranging between 1639 m to 2078 m (**Figure 7**) and they are extended into dry temperate regions having more snowfall in winter. These forests are found in Kuldana, Gharial, Bhurban, Musiari, Masot, Ariari, Hukara Ker, Seribhari, Ghora Gali, Charehan, Darnoian, Patriata, Sangsari and Senewah.

These forests are separated into an upper and lower zones, in each of which definite species of conifers as well as in few oaks dominates. In the upper areas *Abies pindrow* and *Quercus semecarpifolia* are the dominant while in the lower zones, Deodar *(Cedrus deodara)*, *Abies pindrow*, *Picea smithiana* and *Pinus wallichiana* are the key conifer plants in order of increasing elevation, with *Quercus incana* at lower zones or altitudes and *Quercus dilatata* above 2000 m. The temperate deciduous associated tree species include *Aesculus indica*, *Prunus* cornuta, *Quercus incana, Q. dilatata* and few *Juglansregia* and *Ulmus wallichiana* etc are fairly general in these locations. The undergrowth shrubs are hardly dense and comprises of both evergreen as well as deciduous species like *Rubus ellipticus*, *Viburnum cotinifolium*, *Cotoneaster nummularia*, *Berberis calliobotrys*, *Sarcococca saligna*, *Indigofera heterantha*, *Rubus fruticosus, Rosa moschata* and *Rosa macrophylla* etc.

#### **14.2 Subtropical mixed coniferous Forest**

The subtropical pine forests found in between Himalayan moist temperate and sub-tropical broad-leaved forest. The altitudinal ranges of these forests are 1034 m to 1573 m from sea level in the Western Himalayan part and the south-west summer monsoon range (**Figure 8**). Subtropical pine forests are found in majority of the Murree hills which includes; Begla, Bara Hoter, Kasari, Phapril, Khajut, Jaman, Chakka, Birgran and Nandkot. Subtropical pine forest are mostly dominated by *Pinus roxburghii*. These forest type are mostly mixed by *Pinus roxburghii* and other coniferous species like *Pinus wallichiana* at the upper ranges like Dewal, Angoori, Sambli, Nambal, Aucha and Khanitak.

**Figure 8.** *Author in front of mixed coniferous subtropical forests.*

The other associated species of *Pinus roxburghii* are *Quercus glauca*, *Quercus incana*, *Pistaciachinensis* ssp. *integerrima*, *Xylosmalongifolium*, and broad-leaved tree species like *Cornus macrophylla* and *Celtis australis* etc. are also found there. The commonly undergrowth shrub layer consists of *Myrsine africana*, *Carissa spinarum*, *Berberis lycium*, *Rubus ellipticus*, *Dodonaea viscosa*, *Mallotusphilippinenis*,

**Figure 9.** *Subtropical broad leaved forest at Daleh, Murree Hills, Western Himalaya.*

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

*Ziziphus jujuba*, *Daphne papyracea*, *Daphne mucronata* and *Zanthoxylum armatum* etc. Common ground or Herbaceous flora are *Themeda anathera* and *Heteropogoncontortus*.

#### **14.3 Subtropical broad-leaved forests**

The broad-leaved subtropical forests are recorded on the hills and in the lower slopes of Himalaya near Islamabad and Rawalpindi. Subtropical broadleaved forest extended from 520 to 968 m elevation ranges adjacent to the subtropical pine forest in the upper ranges. These type of forest are relatively observed in drier and hot climate conditions with some xerophtic thorny plant species. These forests are found in the areas of Gohi, Simli, Karlot, Baroha, Salgaran, Maanga, Salkheter, Kohatti, Kathar, Daleh and Mangal etc. The subtropical broad leaved forest vegetation is mainly comprised of *Acacia modesta*, *Dodonaea viscosa*, *Woodfordiafruiticosa*, *Ziziphus jujuba*, *Berberis lycium*, *Justicia adhatoda*, *Mallotus philippensis*, *Punica granatum* and *Carissa spinarum*. Noteworthy species that are fastly being removed are *Acacia nilotica*, *Pistacia integerrima*, and *Olea ferruginea*. These forests subsequently hosts undersized shrubs which are oftenly intermittent by herbs and grasses (**Figure 9**).

#### **15. Temporal and spatial vegetation dynamics**

Vegetation in the western Himalayas experienced significant temporal & spatial changes in as shown in **Figures 10** and **11**. NDVI maps indicated that NDVI values for the Murree region in the Pakistani Himalayas varied regularly from −0.45 to 0.74 in the summer. The fluctuation of NDVI values were − 0.30 and 0.80 in the winter. It is apparent that in winter there was increased in vegetation cover due to more rainfall. The NDVI values above zero to one indicated that the forest vegetation increased in their maximum quantity.

The classification results for 1988, 1998, 2008 and 2018 are summarized in **Table 2**. Different classes along with their respective percentage on the basis of these results show the land use land cover practices observed in Murree area during 1988, 1998, 2008 and 2018. The results showed that main decline with respect

**Figure 10.** *Vegetation index in summer.*

**Figure 11.** *Vegetation index in winter.*

#### **Table 2.**

*Land use land cover statistics for four decades (1988–2018) in the Murree.*

to area in Murree was observed in forest vegetation and conversely, the area of bare land, built up/Settlements area and water classes were increased. Vegetation land decreased from 71% to 66.5% of the total area whereas the bare land confronted an increase in the total share from 10.5% to 16.5%. Water class, which was minimum area covering class in 1988, reduced further area under its cover from 1.04% to 0.32% and the built up area was 1.3% of the total area which increased up to 9.5%in the Murree hills, Pakistan (**Figure 12**).

Land use land cover classification results supported the above shown facts that forest cover decreased over the past 30 years by 4.5% from 1988 to 2018. Forest vegetation in the Murree area chiefly includes moist temperate forest, mixed coniferous forest and Subtropical broad-leaved forests. This land cover class was also substituted by built up and bare land class. In addition to cutting of fuel wood by the indigenous communities, deforestation and widespread cattle grazing have malformed the vegetation present in the study area to forest patches, small bushes and several areas have left merely barren lands. The chief accelerators of forest deterioration in the area were the anthropogenic activities like due to high market value; illegitimate forest wood cutting and also the severe use of forest wood for fulfilling domestic necessities like heating and cooking and similarly for timber production. In addition to these ineffectual management of forests also played an important role in forest decrease. Causes were building new recreational areas, housing schemes and farmhouses that have been developed in the area in the past 30 years. Along with these expansions, there is rise in the construction of new roads and highways to access these areas. The area covered by water class has also observed decrease from 1988 to 2018. The land use land cover changes witnessed in all classes affected

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

**Figure 12.** *Land cover classification of the study area (1988–2018).*

the water class during three decades. One reason for the decrease was an increased rate of surface runoff due to deficiency of plant roots to withhold the water. Greater than before deforestation also added to the increase in surface runoff and is accountable for down flow of sediments and nutrients.

*Vegetation Index and Dynamics*

## **Author details**

Amjad ur Rahman1,2\*, Esra Gürbüz3 , Semih Ekercin4 and Shujaul Mulk Khan1

1 Department of Plant Science, Quaid-i-Azam University, Islamabad, Pakistan

2 Department of Botany, University of Swabi, Pakistan

3 Department of Geomatics, Faculty of Engineering, Aksaray University, Turkey

4 Department of Geomatics, Faculty of Engineering, Necmettin Erbakan University, Turkey

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

#### **References**

[1] Hargreaves, P. (2008). Phytosociology in Brazil. J Plant Sci Biotechnol*, 2*, 12-20.

[2] Hussain, F., Saljoqi, A., Shah, A., & Ilahi, I. (1992). Phytosociology of the vanishing sub-tropical vegetation of Swat with special reference to Docut Hills II: Spring aspect [Pakistan]. Sarhad Journal of Agriculture (Pakistan).

[3] Malik, R. N., & Husain, S. Z. (2006). Land-cover mapping: A remote sensing approach. Pakistan Journal of Botany*, 38*(3), 559.

[4] Malik, R. N., & Husain, S. Z. (2008). Linking remote sensing and ecological vegetation communities: A multivariate approach. Pak. J. Bot*, 40*(1), 337-349.

[5] Oommen, M. A., & Shanker, K. (2005). Elevational species richness patterns emerge from multiple local mechanisms in Himalayan woody plants. Ecology*, 86*(11), 3039-3047.

[6] Grubb, P. (1987). *Global trends in species-richness in terrestrial vegetation: a view from the northern hemisphere.* Paper presented at the Symposium of the British Ecological Society.

[7] Malik, Z., Ahmad, S., & Hussain, F. (1994). Present status of subtropical chir pine vegetation of Samani Hills Azad Jammu and Kashmir. Sci. KPK. Pak*, 7*, 51-58.

[8] Kent, M. (2011). *Vegetation description and data analysis: a practical approach*: John Wiley & Sons.

[9] Mandal, G., & Joshi, S. P. (2014). Analysis of vegetation dynamics and phytodiversity from three dry deciduous forests of Doon Valley, Western Himalaya, India. Journal of Asia-Pacific Biodiversity*, 7*(3), 292-304.

[10] Hussain, F., Ilyas, M., & Takatsuki, S. (1997). Plant communities of Girbanr hills, Swat district, northwestern Pakistan. *Ecological Review= Seitaigaku Kenyku, 23*(4), 247.

[11] Gillison, A. (2006). A field manual for rapid vegetation classification and survey for general purposes. *International Forestry*, 3-10.

[12] Mueller-Dombois, D., & Ellenberg, H. (1974). *Aims and methods of vegetation ecology*: Wiley.

[13] Billings, W. D. (1952). The environmental complex in relation to plant growth and distribution. The Quarterly Review of Biology*, 27*(3), 251-265.

[14] Ilyas, M., Qureshi, R., Akhtar, N., Munir, M., & Haq, Z. (2015). Vegetation analysis of Kabal valley, district Swat, Pakistan using multivariate approach. Pakistan J Bot*, 47*, 77-86.

[15] Zhao, J., Ouyang, Z., Zheng, H., Zhou, W., Wang, X., Xu, W., & Ni, Y. (2010). Plant species composition in green spaces within the built-up areas of Beijing, China. Plant ecology*, 209*(2), 189-204.

[16] Ewald, J. (2003). A critique for phytosociology. Journal of Vegetation Science*, 14*(2), 291-296.

[17] Lee, Y. J., & Shukla, S. D. (2005). Pro-and anti-apoptotic roles of c-Jun N-terminal kinase (JNK) in ethanol and acetaldehyde exposed rat hepatocytes. European journal of pharmacology*, 508*(1-3), 31-45.

[18] King, S. L., Antrobus, T. J., & Billups, S. (2000). Vegetation dynamics. *In: Gen. Tech. Rep. SRS-38. Asheville, NC: US Department of Agriculture, Forest Service, Southern Research Station. p. 29-31.*

[19] Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land use change

mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science*, 18*(2), 251-259.

[20] Jan, S., Hamayun, M., Khan, S. A., Ahmad, N., Ahmad, I., & Wali, S. (2015). Plant diversity of Hindu Kush mountain region of Utror and Gabral, Northern Pakistan. Pakistan Journal of Weed Science Research*, 21*(2).

[21] Shehzad, K., Malik, Z., & Qureshi, R. (1999). Phytosociological survey of Samahni valley, Bhimber, Azad Kashmir (Pakistan). *Pakistan Journal of Forestry (Pakistan)*.

[22] Siddiqui, K., Mohammad, I., & Ayaz, M. (1999). Forest ecosystem climate change impact assessment and adaptation strategies for Pakistan. Climate Research*, 12*(2-3), 195-203.

[23] Siddiqui, M. F., Shaukat, S. S., Ahmed, M., Khan, N., & Khan, I. A. (2013). Age and growth rates of dominant conifers from moist temperate areas of Himalayan and Hindukush region of Pakistan. Pak. J. Bot*, 45*(4), 1135-1147.

[24] Krishnaswami, S., Trivedi, J., Sarin, M., Ramesh, R., & Sharma, K. (1992). Strontium isotopes and rubidium in the Ganga-Brahmaputra river system: Weathering in the Himalaya, fluxes to the Bay of Bengal and contributions to the evolution of oceanic87Sr/86Sr. Earth and Planetary Science Letters*, 109*(1-2), 243-253.

[25] Dobremez, J.-F., & Jest, C. (1976). *Manaslu: Hommes et milieux des vallées du Népal central*: FeniXX.

[26] Behera, M. D., & Kushwaha, S. P. S. (2006). An analysis of altitudinal behavior of tree species in Subansiri district, Eastern Himalaya *Plant Conservation and Biodiversity* (pp. 277-291): Springer.

[27] Shinwari, Z. K., & Gilani, S. S. (2003). Sustainable harvest of medicinal plants at Bulashbar Nullah, Astore (northern Pakistan). Journal of Ethnopharmacology*, 84*(2-3), 289-298.

[28] Beg, A. (1975). *Wildlife habitats of Pakistan*: Pakistan Forest Institute.

[29] Joshi, P., Singh, S., Agarwal, S., & Roy, P. (2001). Forest cover assessment in western Himalayas, Himachal Pradesh using IRS 1C/1D WiFS data. Current Science, 941-947.

[30] Prabhakar, R., Somanathan, E., & Mehta, B. S. (2006). How degraded are Himalayan forests? *Current science*, 61-67.

[31] Myers, N. (1986). Environmental repercussions of deforestation in the Himalayas. Journal of World Forest Resource Management*, 2*(1), 63-72.

[32] Cronin, R. P., & Pandya, A. (2009). *Exploiting natural resources: Growth, instability, and conflict in the Middle East and Asia*: Henry L. Stimson Center.

[33] Li, D., Grueter, C. C., Ren, B., Long, Y., Li, M., Peng, Z., & Wei, F. (2008). Ranging of Rhinopithecus bieti in the Samage Forest, China. II. Use of land cover types and altitudes. *International Journal of Primatology, 29*(5), 1147.

[34] Abbasi, A., Khan, M., Ishfaq, M., & Mool, P. (2002). Slope failure and landslide mechanism in Murree area, North Pakistan. Geol Bull Univ Peshawar*, 35*, 125-137.

[35] Pandit, M., Sodhi, N. S., Koh, L. P., Bhaskar, A., & Brook, B. W. (2007). Unreported yet massive deforestation driving loss of endemic biodiversity in Indian Himalaya. Biodiversity and Conservation*, 16*(1), 153-163.

[36] Dhital, D. (2002). *Overview of forest policy reviews in Bhutan.* Paper presented at the Forest Policies and Forest Policy

*Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

Reviews. Proceedings of the Forest Policy Workshop.

[37] Mancino, G., Nolè, A., Ripullone, F., & Ferrara, A. (2014). Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy. Iforest-Biogeosciences and Forestry*, 7*(2), 75.

[38] Shao, Z., & Zhang, L. (2016). Estimating forest aboveground biomass by combining optical and SAR data: a case study in Genhe, Inner Mongolia, China. Sensors*, 16*(6), 834.

[39] Shao, Z., Fu, H., Li, D., Altan, O., & Cheng, T. (2019). Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation. Remote Sensing of Environment*, 232*, 111338.

[40] Zhang, S.-q., Zhang, S.-k., & Zhang, J.-y. (2000). A study on wetland classification model of remote sensing in the Sangjiang Plain. Chinese Geographical Science*, 10*(1), 68-73.

[41] Zhang, Y., Chen, H. Y., & Taylor, A. R. (2017). Positive species diversity and above-ground biomass relationships are ubiquitous across forest strata despite interference from overstorey trees. Functional Ecology*, 31*(2), 419-426.

[42] Ernst-Brock, C., Turner, L., Tausch, R. J., & Leger, E. A. (2019). Long-term vegetation responses to pinyon-juniper woodland reduction treatments in Nevada, USA. Journal of environmental management*, 242*, 315-326.

[43] Teh, S. Y., Turtora, M., DeAngelis, D. L., Jiang, J., Pearlstine, L., Smith, T. J., & Koh, H. L. (2015). Application of a coupled vegetation competition and groundwater simulation model to study effects of sea level rise and storm surges on coastal vegetation. Journal of Marine Science and Engineering*, 3*(4), 1149-1177.

[44] Antilla, L. (2005). Climate of scepticism: US newspaper coverage of the science of climate change. Global environmental change*, 15*(4), 338-352.

[45] Cui, L., Wang, L., Qu, S., Singh, R. P., Lai, Z., & Yao, R. (2019). Spatiotemporal extremes of temperature and precipitation during 1960-2015 in the Yangtze River Basin (China) and impacts on vegetation dynamics. Theoretical and Applied Climatology*, 136*(1-2), 675-692.

[46] Elhakeem, A., & Elshorbagy, W. (2015). Hydrodynamic evaluation of long term impacts of climate change and coastal effluents in the Arabian Gulf. Marine Pollution Bulletin*, 101*(2), 667-685.

[47] Curtis, J. T., & Mcintosh, R. P. (1950). The interrelations of certain analytic and synthetic phytosociological characters. Ecology*, 31*(3), 434-455.

[48] Hjorth, J. U. (1993). *Computer intensive statistical methods: Validation, model selection, and bootstrap*: CRC Press.

[49] Hill, M. O. (1979). *TWINSPAN: A FORTRAN Program for Arranging Multivariate Data in an Ordered Twoway Table by Classification of Individual and Attributes*: Cornell University Press.

[50] Ter Braak, C. T., & Smilauer, P. (1998). CANOCO reference manual and user's guide to Canoco for Windows: software for canonical community ordination (version 4).

[51] Chahouki, M. A. Z. (2013). Classification and ordination methods as a tool for analyzing of plant communities. Multivariate analysis in management, engineering and the sciences, 221.

[52] Dyakov, N. R. (2014). Gradient analysis of vegetation on the south slope of Vitosha Mountain, Southwest Bulgaria. Applied Ecology and

Environmental Research*, 12*(4), 1003-1025.

[53] Khan, W., Khan, S. M., & Ahmad, H. (2015). Altitudinal variation in plant species richness and diversity at Thandiani sub forests division, Abbottabad, Pakistan. Journal of Biodiversity and Environmental Sciences*, 7*(1), 46-53.

[54] Ahmad, S., Mohammad, N., & Khan, S. (2013). Evaluating the effecst of soil pH and moisture on the plant species of Changa Manga Forest using Van Dobben circles. Middle East Journal of Scientific Research*, 17*(10), 1405-1411.

[55] Arifa, Z., Almas, J., Sheikh, S. A., & Zaheer-ud-Din, K. (2018). Floral and edaphic data recorded from Sahiwal district, Pakistan analyzed by multivariate techniques. Biologia (Pakistan)*, 64*(1), 173-177.

[56] Urooj, R., Ahmad, S. S., Ahmad, M. N., Ahmad, H., & Nawaz, M. (2016). Ordination study of vegetation analysis around wetland area: a case study of Mangla dam, Azad Kashmir, Pakistan. Pakistan Journal of Botany*, 48*(1), 115-119.

[57] Urooj, R., Ahmad, S. S., Ahmad, M. N., & Khan, S. (2015). Ordinal classification of vegetation along Mangla dam, Mirpur, AJK. *Pak. J. Bot, 47*(4), 1423-1428.

[58] David, V. (2017). *Data treatment in environmental sciences*: Elsevier.

[59] Greig-Smith, P. (1983). *Quantitative plant ecology* (Vol. 9): Univ of California Press.

[60] Tüxen, R., & Whittaker, R. (1973). *Handbook of vegetation science*: Springer.

[61] Miyawaki, A., & Fujiwara, K. (1988). Vegetation mapping in Japan *Vegetation mapping* (pp. 427-441): Springer.

[62] Khan, S. M., Harper, D., Page, S., & Ahmad, H. (2011b). Species and community diversity of vascular flora along environmental gradient in Naran Valley: A multivariate approach through indicator species analysis. Pak. J. Bot*, 43*(5), 2337-2346.

[63] Rieley, J. O., & Page, S. E. (1990). *Ecology of plant communities. A phytosociological account of the British vegetation*: Longman Scientific and Technical.

[64] Bhadra, A., & Dhal, N. (2010). Tree seedling diversity in the protected natural forest of Gandhamardan hill ranges. The Bioscan*, 2*, 451-459.

[65] Pahwa, J. S., Brewer, P., Sutton, T., Yesson, C., Burgess, M., Xu, X., … Fiddian, N. J. (2006). *Biodiversity World: a problem-solving environment for analysing biodiversity patterns.* Paper presented at the Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

[66] Mucina, L. (1997). Classification of vegetation: Past, present and future. Journal of Vegetation Science*, 8*(6), 751-760.

[67] Phillips, O. L., Vásquez Martínez, R., Monteagudo Mendoza, A., Baker, T. R., & Núñez Vargas, P. (2005). Large lianas as hyperdynamic elements of the tropical forest canopy. Ecology*, 86*(5), 1250-1258.

[68] Baillie, J. E., Hilton-Taylor, C., & Stuart, S. N. (2004). 2004 IUCN red list of threatened species.

[69] Zou, D., He, Y., Lin, Q., & Cui, G. (2007). Evaluation of the level of threat and protective classification of the vegetation of Makehe Forest in Sanjiangyuan Nature Reserve, west China. Frontiers of Forestry in China*, 2*(2), 179-184.

[70] Feola, S., Carranza, M. L., Schaminée, J., Janssen, J., & Acosta, A. *Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

(2011). EU habitats of interest: an insight into Atlantic and Mediterranean beach and foredunes. Biodiversity and Conservation*, 20*(7), 1457-1468.

[71] Vačkář, D., ten Brink, B., Loh, J., Baillie, J. E., & Reyers, B. (2012). Review of multispecies indices for monitoring human impacts on biodiversity. Ecological Indicators*, 17*, 58-67.

[72] Khan, S. M., Page, S., Ahmad, H., & Harper, D. (2014). Ethno-ecological importance of plant biodiversity in mountain ecosystems with special emphasis on indicator species of a Himalayan Valley in the northern Pakistan. Ecological indicators*, 37*, 175-185.

[73] Dasti, A. A., Saima, S., Mahmood, Z., Athar, M., & Gohar, S. (2010). Vegetation zonation along the geological and geomorphological gradient at eastern slope of Sulaiman Range, Pakistan. African Journal of Biotechnology*, 9*(37), 6105-6115.

[74] Ren, H. B., Niu, S. K., Zhang, L. Y., & Ma, K. P. (2006). Distribution of vascular plant species richness along an elevational gradient in the Dongling Mountains, Beijing, China. Journal of Integrative Plant Biology*, 48*(2), 153-160.

[75] Siddiqui, M. F., Ahmed, M., Wahab, M., Khan, N., Khan, M. U., Nazim, K., & Hussain, S. S. (2009). Phytosociology of Pinus roxburghii Sargent (chir pine) in lesser Himalayan and Hindu Kush range of Pakistan. Pak. J. Bot*, 41*(5), 2357-2369.

[76] Haarmeyer, D. H., Schmiedel, U., Dengler, J., & Bösing, B. M. (2010). How does grazing intensity affect different vegetation types in arid Succulent Karoo, South Africa? Implications for conservation management. Biological Conservation*, 143*(3), 588-596.

[77] Pinke, G., & Pal, R. (2008). Phytosociological and conservational study of the arable weed communities in western Hungary. Plant Biosystems-An International Journal Dealing with all Aspects of Plant Biology*, 142*(3), 491-508.

[78] Lambers, H., Chapin III, F. S., & Pons, T. L. (2008). *Plant physiological ecology*: Springer Science & Business Media.

[79] Jongkind, C., & Hawthorne, W. (2005). A botanical synopsis of the lianes and other forest climbers. Forest climbing plants of West Africa: diversity, ecology and management, 19-39.

[80] Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological modelling*, 135*(2-3), 147-186.

[81] Zhang, H., Qian, Y., Wu, Z., & Wang, Z. (2012). Vegetationenvironment relationships between northern slope of Karlik Mountain and Naomaohu Basin, East Tianshan Mountains. Chinese Geographical Science*, 22*(3), 288-301.

[82] Enright, N., Miller, B., & Akhter, R. (2005). Desert vegetation and vegetation-environment relationships in Kirthar National Park, Sindh, Pakistan. Journal of Arid Environments*, 61*(3), 397-418.

[83] Jabeen, T., & Ahmad, S. (2009). Multivariate analysis of environmental and vegetation data of Ayub National Park Rawalpindi. Soil and Environment (Pakistan).

[84] Jones, M. M., Tuomisto, H., Clark, D. B., & Olivas, P. (2006). Effects of mesoscale environmental heterogeneity and dispersal limitation on floristic variation in rain forest ferns. Journal of ecology*, 94*(1), 181-195.

[85] Tavili, A., ROSTAMPOUR, M., ZARE, C. M., & Farzadmehr, J. (2009). CCA application for vegetationenvironment relationships evaluation in arid environments (southern Khorasan rangelands).

[86] Zhang, J.-T., & Zhang, F. (2011). Ecological relations between forest communities and environmental variables in the Lishan Mountain Nature Reserve, China. African Journal of Agricultural Research*, 6*(2), 248-259.

[87] Hejcmanovā-Nežerková, P., & Hejcman, M. (2006). A canonical correspondence analysis (CCA) of the vegetation–environment relationships in Sudanese savannah, Senegal. South African Journal of Botany*, 72*(2), 256-262.

[88] Eilu, G., Hafashimana, D. L., & Kasenene, J. M. (2004). Density and species diversity of trees in four tropical forests of the Albertine rift, western Uganda. Diversity and Distributions*, 10*(4), 303-312.

[89] Heydari, M., & Pourbabei, H. (2010). Vegetation-environmental relationships and ecological species groups of the Ilam oak forest landscape, Iran. Caspian Journal of Environmental Sciences*, 8*(2), 115-125.

[90] Nguyen, T. V., Mitlohner, R., Bich, N. V., & Do, T. V. (2015). Environmental factors affecting the abundance and presence of tree species in a tropical lowland limestone and non-limestone forest in Ben En National Park, Vietnam. Journal of forest and environmental science*, 31*(3), 177-191.

[91] Boulangeat, I., Gravel, D., & Thuiller, W. (2012). Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances. Ecology letters*, 15*(6), 584-593.

[92] Hotra, L., Kolasky, E., & Voss, J. (2003). Abiotic factors influencing the abundance and distribution of marsh marigolds along Carp Creek.

[93] Muzein, B. S. (2006). *Remote sensing & GIS for land cover, land use change detection and analysis in the semi-natural ecosystems and agriculture landscapes of the central Ethiopian Rift Valley.* Verlag nicht ermittelbar.

[94] Roy, P., & Ravan, S. A. (1994). Habitat Management for Biodiversity Maintenance using aerospace remote sensing. Tropical ecosystems: A synthesis of tropical ecology and conservation, 309-345.

[95] Roy, P. (1993). Remote sensing for forest ecosystem analysis and management. Environmental studies in India, 335-363.

[96] Schaepman, M. E. (2005). *Imaging spectroscopy in ecology and the carbon cycle: Heading towards new frontiers.* Paper presented at the Proceedings of the EARSeL 4th workshop on Imaging Spectroscopy, Warsaw, Poland.

[97] Salem, B. (2003). Application of GIS to biodiversity monitoring. Journal of Arid Environments*, 54*(1), 91-114.

[98] Codjoe, S. N. A. (2007). Integrating remote sensing, GIS, census, and socioeconomic data in studying the population–land use/cover nexus in Ghana: A literature update. Africa Development*, 32*(2).

[99] Milla, K. A., Lorenzo, A., & Brown, C. (2005). GIS, GPS, and remote sensing technologies in extension services: Where to start, what to know. Journal of extension*, 43*(3), 2.

[100] Zinner, D. (1996). GIS and remote sensing techniques as tools for surveying primates. Ecotropica*, 2*, 41-47.

[101] Cai, G., Ren, H., Yang, L., Zhang, N., Du, M., & Wu, C. (2019). Detailed urban land use land cover classification *Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan DOI: http://dx.doi.org/10.5772/intechopen.98401*

at the metropolitan scale using a three-layer classification scheme. Sensors*, 19*(14), 3120.

[102] Tapiador, F. J., & Casanova, J. L. (2003). Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain. Landscape and urban planning*, 62*(2), 103-115.

[103] Agrawal, A. A., Ackerly, D. D., Adler, F., Arnold, A. E., Cáceres, C., Doak, D. F., … Mooney, K. A. (2007). Filling key gaps in population and community ecology. Frontiers in Ecology and the Environment*, 5*(3), 145-152.

[104] Austin, M. (2007). Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological modelling*, 200*(1-2), 1-19.

[105] Osborne, P. E., Foody, G. M., & Suárez-Seoane, S. (2007). Nonstationarity and local approaches to modelling the distributions of wildlife. Diversity and Distributions*, 13*(3), 313-323.

[106] Shahid, M. (2011). Monitoring deforestation and urbanization growth in rawal watershed area using remote sensing and gis techniques.

[107] Zafar, S. M., Baig, M. A., & Irfan, M. (2011). *Application of GIS/RS for management zoning of Margallah Hills National Park, Islamabad.* Paper presented at the International Conference on Environmental Science and Technology.

[108] Wardlow, B. D., Egbert, S. L., & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment*, 108*(3), 290-310.

[109] Ashraf, M., Kahlown, M. A., & Ashfaq, A. (2007). Impact of small dams on agriculture and groundwater development: A case study from Pakistan. Agricultural Water Management*, 92*(1-2), 90-98.

[110] Noss, R. F. (1990). Indicators for monitoring biodiversity: a hierarchical approach. Conservation biology*, 4*(4), 355-364.

[111] Powell, G. V., Barborak, J., & Rodriguez, M. (2000). Assessing representativeness of protected natural areas in Costa Rica for conserving biodiversity: a preliminary gap analysis. Biological Conservation*, 93*(1), 35-41.

[112] Ramesh, B., Menon, S., & Bawa, K. S. (1997). A vegetation based approach to biodiversity gap analysis in the Agastyamalai region, Western Ghats, India. *Ambio*, 529-536.

[113] Champion, S. H., Seth, S. K., & Khattak, G. (1965). Forest types of Pakistan. *Forest types of Pakistan.*

[114] CI-IAGHTAI, S., SHAH, S. H., & AKIITAR, M. (1978). PHYTOSOCIOLOGICAL STUDY OF 'THE GRAVEYARDS OF PESHAWAR DISTRICT, NWFP, PAKISTAN.

[115] Zeitler, P. K. (1985). Cooling history of the NW Himalaya, Pakistan. Tectonics*, 4*(1), 127-151.

[116] Khan, A. N., Collins, A. E., & Qazi, F. (2011a). Causes and extent of environmental impacts of landslide hazard in the Himalayan region: a case study of Murree, Pakistan. Natural Hazards*, 57*(2), 413-434.

[117] Farooq, S., & Malik, M. (1996). Landslide Hazard Management and Control in Pakistan-A Review.

[118] Khan, A. N. (1992). *Housing and landslides: a case study of Murree, Pakistan.* University of Nottingham.

[119] Saqib, Z., Mahmood, A., Malik, R. N., Mahmood, A., Syed, J. H., & Ahmad, T. (2014). Indigenous knowledge of medicinal plants in Kotli Sattian, Rawalpindi district, Pakistan. Journal of Ethnopharmacology*, 151*(2), 820-828.

[120] Ahmad, T. (2011). Geology of the Himalayan Mountain Range, with special reference to the western Himalaya. Geologi*, 63*, 142-147.

[121] Gansser, A. (1964). Geology of the Himalayas.

[122] Ishfaq, M. (1999). Application of GIS and RS to slope instability problems (Landsliding). *Northeastern Murree, NWFP, Pakistan*.

[123] Clift, P. D., Giosan, L., Blusztajn, J., Campbell, I. H., Allen, C., Pringle, M., … Alizai, A. (2008). Holocene erosion of the Lesser Himalaya triggered by intensified summer monsoon. Geology*, 36*(1), 79-82.

[124] Syed, F. S., Yoo, J. H., Körnich, H., & Kucharski, F. (2010). Are intraseasonal summer rainfall events micro monsoon onsets over the western edge of the South-Asian monsoon? Atmospheric research*, 98*(2-4), 341-346.

[125] Archer, D. R., & Fowler, H. J. (2008). Using meteorological data to forecast seasonal runoff on the River Jhelum, Pakistan. Journal of Hydrology*, 361*(1-2), 10-23.

[126] Association, W. M. (2013). World Weather Information Service. *Retrieved May, 30*.

[127] Shaheen, H., Khan, S. M., Harper, D. M., Ullah, Z., & Qureshi, R. A. (2011). Species diversity, community structure, and distribution patterns in western Himalayan alpine pastures of Kashmir, Pakistan. Mountain Research and Development*, 31*(2), 153-159.

[128] Shaheen, H., Qureshi, R., Iqbal, S., & Qasem, M. F. (2014). Seasonal availability and palatability of native flora of Santh Saroola Kotli Sattian, Rawalpindi, Pakistan. African Journal of Plant Science*, 8*(2), 92-102.

[129] Ullah, R. (2009). *Evaluation Of Soil Productivity Under Different Cropping Patterns On Sloppy Lands (Terraces) In Pothowar.* Arid Agriculture University, Rawalpindi.

[130] Hamayun, M., Khan, M. A., & Begum, S. (2003). Marketing of medicinal plants of Utror-Gabral valleys, Swat, Pakistan. Ethnobotanical Leaflets*, 2005*(1), 44.

#### **Chapter 12**


**4. Conclusion**

148 Artificial Photosynthesis

peridinin, allene modified and polyene chain modified derivatives were recorded in methyl methacrylate polymer at 77 K. The || values were corresponding to the CT absorption band. As the results of peridinin and allene modified derivatives, peridinin showed the largest || value among all of them. Namely, peridinin yielded a || value of 5.42 (x 10- 29 C・m), acetylene derivative **2** showed 2.47, olefin derivative **3** showed 4.22, and diolefin derivative **4** showed 4.25. A || value of peridinin was in agreement with a reported value (Premvardhan et al., 2005). The || value generally shows a larger number with the increasing -electron chain length theoretically. Although peridinin possesses fewer conjugating double bonds and shows a shorter max rather than that of diolefin derivative **4**, the || value of peridinin was the largest among the four compounds. The difference in the || value is evidently attributable to the difference in the functional groups. Thus, we have understood that the unique allene group contributes to production of the large dipole moment in the molecule. These results strongly suggest that the allene group of peridinin is essential for formation of the effective ICT state, which would allow the quantitative energy transfer to Chl a in the PCP complex. This is the first experimental evidence that shows the

allene group in peridinin enhances the ICT character (Kusumoto et al., 2010).

answer to the question of why peridinin (**1**) possesses the irregular C37 skeleton.

  Namely, peridinin (**1**) yielded a || value of 5.42 (x 10-29 C・m), C35 peridinin derivative **6** showed 4.25, and C39 peridinin derivative **7** did 5.29. The || value generally shows a larger number with the increasing -electron chain length (Kajikawa et al., 2009b). Although peridinin possesses fewer conjugated double bonds and shows a rather shorter max than that of C39 peridinin derivative **7**, the || value of peridinin (**1**) was the largest among the three compounds. Thus, the C37 skeleton of peridinin (**1**) would also contribute to the large dipole moment of the molecule in the exited state to facilitate energy transfer. This would be at least a partial The research on the efficient energy transfer mechanism of the primary photochemical events of photosynthesis have been exactly developed by the new approach based on the synthetic organic chemistry. Namely, the relationship between the characteristic structure of peridinin and the nature of its ICT state has been gradually made clear by synthesizing a series of peridinin analogues and comparing their characteristics to those of peridinin.

Stark spectra of peridinin, allene modified, and polyene chain modified derivatives have showed that the dipole moment of the excited state (|| value) of peridinin is the largest among the series of six compounds, although the increasing -electron chain length generally shows a larger value of ||. These results apparently show that the allene group and C37 skeleton of peridinin effectively contributes to production of the large dipole moment in the molecule in excited state, which would result in the high energy transfer efficiencies to Chl a in the PCP complex. This is an answer of why peridinin possesses the unique allene bond and the irregular C37 skeleton.

In addition, the ultrafast time resolved optical absorption spectra of polyene chain modified derivatives including peridinin show that the lifetime of the lowest excited singlet state of C33 peridinin derivative has the strongest solvent dependence so far yet reported. Furthermore, the data reveal the striking observation that the lifetime of the ICT state converges to a value of 10 1 ps in methanol for all peridinin analogues regardless of the extent of -electron conjugation. These data strongly support the notion that the S1 and ICT states behave independently.

On the other hand, comparing the stereochemical stability and spectral characteristics of the synthesized ylidenebutenolide modified analogues to those of peridinin has resulted in the conclusion that this particular functional group at least contributes to maintaining the stereochemistry of the conjugated double bonds in the all-*trans* configuration and giving rise to a max value desirable for the marine organism to absorb light in the blue-green region of the visible spectrum.

These inherent characteristics of peridinin are important clues for elucidating the energy transfer mechanism from the light-harvesting carotenoids to chlorophylls. The studies to measure the energy transfer efficiencies of peridinin derivatives are currently in progress to further understand the exact role of these unique functional groups.


**Author details**

Julia N. Soulakova\* and Trung Ha College of Medicine, University of Central Florida, Orlando, FL, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**173**

*On Statistical Assessments of Racial/Ethnic Inequalities in Cigarette Purchase Price among Daily…*

for clinical trials that do not require all primary effects to be significant. Statistics in Medicine. 2003;**22**(15):2387-2400. Available from: http://doi.wiley.com/10.1002/sim.1526

[Accessed: 22 February 2018]

[9] Soulakova JN. Comparison of gatekeeping and other testing methods for identifying superior drug combinations in bifactorial designs with isotonic parameters. Journal of Biopharmaceutical Statistics.

[10] Chen X, Luo X, Capizzi T. The application of enhanced parallel gatekeeping strategies. Statistics in Medicine. 2005;**24**(9):1385-1397. Available from: http://doi.wiley.com/10.1002/ sim.2005 [Accessed: 22 February 2018]

[11] Sugitani T, Morikawa T. Gatekeeping strategies and graphical approaches in clinical trials with hierarchically structured study objectives: A review. Japanese Journal of Biometrics.

[12] Hsu JC. Multiple Comparisons: Theory and Methods. New York: CRC

[13] Soulakova JN. On identifying

[14] Holm S. A simple sequentially rejective multiple test procedure on JSTOR. Scandinavian Journal of Statistics. 1979;**6**(2):65-70.

Available from: https://www.jstor.org/ stable/4615733?seq=1#page\_scan\_tab\_ contents [Accessed: 25 July 2018]

[15] Johnson RA, Wichern DW. Applied Multivariate Statistical Analysis. Upper Saddle River, New Jersey: Prentice Hall;

2002. pp. 232-234

effective and superior drug combinations via Holm's procedure based on the Min tests. Journal of Biopharmaceutical Statistics. 2009;**19**(2):280-291

2011;**21**(4):635-649

2017;**38**(1):41-78

Press; 1996

*DOI: http://dx.doi.org/10.5772/intechopen.93380*

[1] Golden SD, Kong AY, Ribisl KM. Racial and ethnic differences in what smokers report paying for their cigarettes. Nicotine & Tobacco Research. 2016;**18**(7):1649-1655

[2] Soulakova JN, Li J, Crockett LJ. Race/ ethnicity and intention to quit cigarette smoking. Preventive Medicine Reports. 2017;**5**:160-165. Available from: http:// linkinghub.elsevier.com/retrieve/pii/

S2211335516301632

**References**

December 2019]

2011;**21**(4):768-786

Wiley; 1987

[3] Cokkinides VE, Halpern MT, Barbeau EM, Ward E, Thun MJ. Racial and ethnic disparities in smokingcessation interventions: Analysis of the 2005 National Health Interview Survey. American Journal of Preventive

Medicine. 2008;**34**(5):404-412

[5] Dmitrienko A, Soulakova JN, Millen BA. Three methods for constructing parallel gatekeeping procedures in clinical trials. Journal of Biopharmaceutical Statistics.

[6] Hochberg Y, Tamhane A. Multiple Comparison Procedures. New York:

[7] Dmitrienko A, Offen W, Wang O, Xiao D. Gatekeeping procedures in dose–response clinical trials based on the Dunnett test. Pharmaceutical Statistics. 2006;**5**(1):19-28. Available from: http://doi.wiley.com/10.1002/ pst.190 [Accessed: 22 February 2018]

[8] Dmitrienko A, Offen WW, Westfall PH. Gatekeeping strategies

[4] Tran S-TT, Rosenberg KD, Carlson NE. Racial/ethnic disparities in the receipt of smoking cessation interventions during prenatal care. Maternal and Child Health Journal. 2010;**14**(6):901-909. Available from: http://link.springer.com/10.1007/ s10995-009-0522-x [Accessed: 10

*On Statistical Assessments of Racial/Ethnic Inequalities in Cigarette Purchase Price among Daily… DOI: http://dx.doi.org/10.5772/intechopen.93380*


*3.3. Comparison and Analysis of Various Models*

The variable selection method was selected to filter the characteristic variables of the sensor data, and the PLS prediction model of the number of days of apple spoilage was established. The specific results of the apple spoilage time prediction model established by the variable selection method are shown in Table 2. The scatter plot of the apple spoilage time prediction model is shown in Figure 6, in which the Rc and Rp of ACO-PLS are 0.971 and 0.926, respectively, the Rc and Rp of SA-PLS are 0.942 and 0.936, respectively, and ACO-PLS has the highest Rc, but Rp is low. In order to ensure the prediction accuracy of

the apple spoilage model, the variables were numbered from 1 to 3000, and SA-PLS was used to establish an early warning model of apple spoilage. The characteristic variables were identified to be 1889, 1894, 1974, 2001, 2159, 2163, 2274, 2561, 2758 and 2965. A similar observation was made by Guo et al. [41], where they observed that the competitive adaptive reweighted sampling (CARS) algorithm combined with PLS effectively filtered irrelevant information and improved the accuracy of the model in predicting apple spoilage area from the electronic nose data. Table 3 showed the characteristic variables and original variable ranges screened by SA.

**Table 2.** Prediction model results of days before apple spoilage using C2H4, CO2, VOC and O2 sensor data.

**Table 3.** Characteristic variables and original variable ranges of each sensor in the apple spoilage early warning model.

According to the results of the SA-PLS spoilage early warning model, the dependent variables, independent variables, and coefficients of the model were derived. The model results are shown in Table 4. The apple spoilage early warning model is as follows: Y = 0.3264 X1 + 0.3708 X2 + 0.0248 X3 + 0.0363 X4 − 0.0008 X5 − 0.0005 X6 − 0.0014 X7 + 0.4734 X8 + 0.3338 X9 +0.0248 X10 − 0.0136 X11 − 0.0118 X12 − 0.0132 X13 + 0.3407 X14 − 1.9581 X15 + 0.3719 X16 + 0.5173 X17 − 1.9010 X18 + 0.0013 X19 − 0.0009 X20 + 38.9899. Among these values, X1–X20 are the dependent variables, that is, the value of the sensor corresponding to the screening feature variable. When the value of Y is in the range of 1–8, the Y value from 1–2 indicates the freshness of the product, whereas a value between 3 and 4 indicates spoilage. Similarly, a value of 5–6 indicates that the spoilage grade is medium spoilage, and a value of 7–8 indicates that the spoilage grade is severe spoilage.

**Table 4.** Independent variables, dependent variables and coefficients of apple spoilage early warning model.


*Article* **Comparative Study of High-Performance Concrete Characteristics and Loading Test of Pretensioned Experimental Beams**

**Pavlina Mateckova 1,\*, Vlastimil Bilek 2 and Oldrich Sucharda 2,3**

**Abstract:** High-performance concrete (HPC) is subjected to wide attention in current research. Many research tasks are focused on laboratory testing of concrete mechanical properties with specific raw materials, where a mixture is prepared in a relatively small amount in ideal conditions. The wider utilization of HPC is connected, among other things, with its utilization in the construction industry. The paper presents two variants of HPC which were developed by modification of ordinary concrete used by a precast company for pretensioned bridge beams. The presented variants were produced in industrial conditions using common raw materials. Testing and comparison of basic mechanical properties are complemented with specialized tests of the resistance to chloride penetration. Tentative expenses for normal strength concrete (NSC) and HPC are compared. The research program was accomplished with a loading test of model experimental pretensioned beams with a length of 7 m made of ordinarily used concrete and one variant of HPC. The aim of the loading test was to determine the load–deformation diagrams and verify the design code load capacity calculation method. Overall, the article summarizes the possible benefits of using HPC compared to conventional concrete.

**Keywords:** high-performance concrete; mechanical properties; loading test; pretensioned beam


*2.4. Aspen Plus Model*

The energy aspects of pyrolysis were thermodynamically investigated by performing some Aspen Plus simulations reproducing the pyrolysis of hydrochars. The model is based on thermodynamic equilibrium and the minimization of the Gibbs energy, without considering any kinetic phenomena. In particular, the thermodynamic properties of the conventional components were calculated by adopting the Peng–Robinson method.

Simulations were performed on grape seeds, hydrochars, and a commercial coal. Table 2 reports a summary of all input data, while Figure 1 shows the general Aspen scheme. The stream REACTANT represents the input feedstock entering the process, defined through a non-conventional stream characterized by its ultimate analysis and HHV (grape seeds and hydrochar in Table 1, coal Illinois No. 6 from literature [40]). Then, the RYIELD reactor, which is fictitious, splits the non-conventional stream into a conventional one (ELEMENTS) based on its elemental distribution. This stream enters the RGIBBS reactor, which computes the product distribution as a function of the operating conditions (i.e., temperature and pressure) by minimizing the Gibbs energy of the product mixture. The final products considered in the model are carbon graphite, carbon dioxide (CO2), carbon monoxide (CO), water (H2O), hydrogen (H2), oxygen (O2), methane (CH4), acetylene (C2H2), ethylene (C2H4), and ethane (C2H6), to ensure a good model performance. Then, the net heat required/produced by the process is given by stream QOUT, while QIN represents the heat required to sustain RYIELD. Runs were performed between 300 and 900 ◦C (every block and stream were kept isotherm to avoid any sensible heat contribution) to deduce the amount of heat involved during the reaction. The product distribution at different temperatures was also investigated. Data were referred per gram of dry feedstock entering the process.

**Table 2.** Summary of parameters used in the Aspen Plus simulations.

**Figure 1.** Aspen Plus scheme used to model pyrolysis.