**1. Introduction**

The conversion of one land cover type to another is one of the most visible and rapid changes that the earth is experiencing and these conversions have profound social and environmental impacts at multiple scales. In the past, the drivers of land cover changes have been classified into two categories: proximate and distant, or indirect [1]. Recently, the phenomenon of tele-coupling between places has been recognized as key to understanding how distant drivers relate to proximate drivers and influence local landscape changes [2–4]. Some of the key drivers of landscape changes include economic, technological, institutional and policy, cultural, and demographic factors [5].

The growth of urban areas leads to land cover change in many parts of the world, especially in developing countries [3,6]. Intense urbanization and increase in anthropogenic activities reflect the scope, intensity, and frequency of human interference, and the changes they cause in ecological processes and systems in the urbanized areas [7]. The urban areas consist of only 1–6% of the earth's land surface, ye<sup>t</sup> they have enormous impacts on the functioning and service of local and global ecosystems, by modifying local climate conditions, eliminating and fragmenting native habitats, generating anthropogenic pollutants, etc. [8]. The spatial pattern of an urban landscape is a result of the

interaction between various driving forces including natural and socioeconomic factors [9]. Increasing trends in industrialization and urbanization, along with the migration from rural to urban areas, are the most dominant factors influencing the land cover transformation. In the rural areas, employment opportunities and income are insu fficient, which contributes to large di fferences in income and facility levels between urban and rural areas [10]. In developing countries, new cities are being developed due to human migration, infrastructure development, and growing job opportunities [10,11].

The United Nations (UN) Sustainable Development Goals (SDGs) are a collection of 17 global goals, which include 232 indicators set by the UN General Assembly for the year 2030. These goals are an urgen<sup>t</sup> call for action by all countries—developed and developing—in a global partnership. Pakistan is a signatory to the UN SDGs, and this study analyzes the land use change dynamics of Islamabad Capital Territory (ICT) in the context of the relevant SDGs and indicators. The Goal 11 of the SDGs, "Make cities and human settlements inclusive, safe, resilient and sustainable", with indicator number 11.3.1 "Ratio of Land Consumption Rate to Population Growth Rate (LCRPGR)" [12] is an important parameter for analyzing sustainability of land use and land change with population growth. The LCRPGR parameter is actually based on the previously defined parameters of LCR [13,14] and PGR [15], and several studies of urban areas have been carried out before on the basis of these parameters. The LCRPGR parameter and terminology has been given prime importance now after being linked with the UN defined SDGs. LCRPGR is vital to understand the rate of land change as compared to the population boom, to understand historical land consumption traditions, and to guide decision and policy makers on the planned expansion of the city along with the protection of environmental, social, and economic assets. Another SDG, Goal 15, "Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss" emphasizes the protection of tree cover and sustainable managemen<sup>t</sup> of forests. The SDG indicator number 15.2.1, "Progress towards sustainable forest management" [16] calls for sustainable managemen<sup>t</sup> strategies for conserving the forest cover, enhancing environmental education, and engaging a wide range of stakeholder institutions, policies, regulations, and considerations that promote sustainability and utilization of natural resources at multiple spatial scales [17].

Assessment and monitoring of land cover dynamics is essential for the sustainable managemen<sup>t</sup> of natural resources, environmental protection, biodiversity conservation, and developing sustainable livelihoods. Therefore, the development of applicable and systematic methods for producing and updating land cover databases are considered an urgen<sup>t</sup> need [18]. Around the globe, urban land expansion rates are higher than or equal to urban population growth rates [19–21]. Many research studies focus on big cities and metropolises, where increase in population through analysis of census statistics is directly linked with the urban land expansion; however, these statistics do not provide information regarding spatial distribution, pattern, and scale of urban land use change. Multi-temporal land cover change analysis and simulation based on coarse to very high resolution satellite remote sensing images is becoming a well established technique for quantifying changes occurring on the earth's surface, and multi-temporal aerial and satellite datasets are now widely and continuously being used for urban growth mapping, monitoring, and modeling with a focus on the spatial dimension and structures [22–25]. In urban expansion studies, spatiotemporal analysis of land cover and land use changes has helped towards understanding the underlying natural and socio-economic factors and drivers. For instance, Seto et al. [26] have presented a meta-analysis of 326 studies which used temporal satellite images to map urban land conversion. A total of 58,000 km<sup>2</sup> increase in urban land area was reported in thirty years (1970 to 2000) and by 2030, global urban land cover is expected to increase between 430,000 km<sup>2</sup> and 12,568,000 km2, with an estimate of 1,527,000 km<sup>2</sup> more likely. According to Seto et al. [26], across all regions and for all three decades, urban land expansion rates are higher than or equal to urban population growth rates. Yang et al. [27] studied and reported the evidence of urban agglomerations through satellite images in four major bay areas of US (San Francisco and New York), China (Hong Kong-Macau), and Japan (Tokyo), from 1987 to 2017.

Clarke et al. [28] proposed a framework to combine remote sensing and spatial metrics for improved understanding and representation of urban dynamics to come up with alternative conceptions of urban spatial structure and change. Particularly with regards to urban forestry, a few studies have focused towards the assessment, mapping, and monitoring of urban forest parameters in fast-growing cities of developing countries. For example, Gong et al. [29] carried out a 30-year forest fragmentation study over Shenzhen Special Economic Zone (SEZ), a city which was established in 1979 in Southern China. Huang et al. [30] utilized satellite images of 77 metropolitan areas in Asia, US, Europe, Latin America, and Australia to calculate and analyze seven spatial metrics (area weighted mean shape, area weighted mean patch fractal dimension index, centrality, compactness index, compactness index of the largest patch, ratio of open space, and density). According to their analysis, the compactness, density, and regularity of urban areas in developing regions generally exceeded the levels reported throughout developed countries [30]. Dewan et al. [31] studied the dynamics of land use/cover changes though landscape fragmentation analysis in Dhaka Metropolitan, Bangladesh, computing and analyzing the following metrics: Number of patches, Patch density, Landscape shape index, Largest patch index, Mean patch size, Area-weighted mean fractal dimension, Interspersion and juxtaposition, Contagion, and Shannon's diversity index.

In Pakistan, like other developing countries, most urban development is haphazard, typically lacking appropriate planning strategies [10]. Pakistan's urbanization rate is the highest in South Asia, and by 2030, Pakistan will have more people in cities than in rural areas. Growing population and rapid development is causing prime agricultural land to be encroached and also causing loss of tree cover [32–36]. In the late 1960s, the capital of the Islamic Republic of Pakistan was shifted from Karachi to Islamabad (o fficially named Islamabad Capital Territory (ICT)). The masterplan of ICT was developed by the famous Greek architect and town planner C. A. Doxiadis [37]. In terms of a planned new capital, ICT is similar to planned new post-colonial capitals/relocations as in the cases of Brasilia (Brazil), Nur-Sultan (named as Astana from 1998 to 2019, and Akmola previously) in Kazakhstan, and Canberra (Australia) [38–40]. In the recent decades, with various ongoing development activities, ICT has been struggling with rapid urbanization and gigantic levels of pollution from industrial, residential, and transportation sources. In terms of population, ICT is considered as the most diverse city of Pakistan with a large percentage of immigrants and foreigner population [41]. Unprecedented influx of migrants and population increase has resulted in urban sprawl and conversion of fertile agricultural land and green cover into concrete—a clear deviation from the original ICT master plan [39,40]. Uncontrolled population growth in ICT due to rapid urbanization has deteriorated the living environment, and increased the adverse ecological impacts on human health, flora, and fauna [42].

#### *1.1. Literature Review—ICT Mapping and Monitoring*

In the last 20 years, several studies have been conducted on the ICT, regarding land cover change, biomass estimation, water quality monitoring, and temperature increase using satellite datasets. In this section, we present a synthesis of published work regarding land cover change dynamics in the ICT.

Adeel [43] identified urban growth potential through land use for ICT zone IV (Figure 1), based on SPOT-5 2.5 m panchromatic dataset and population census data, and found that nearly 63% of zone IV carries a 'High' to 'Very High' future growth potential, which is mainly located close to Islamabad Expressway. This work uses satellite imagery and field data from one year (2007) and does not report spatiotemporal change dynamics. Butt et al. [35] studied the metropolitan development in ICT, based on growth direction and expansion trends from the city center, for the period 1972–2009 using Landsat satellite images. Using Principal Components Analysis (PCA), band ratios, and supervised classification methods, they found that the urban development had expanded by 87.31 km<sup>2</sup> in 38 years. Butt et al. [44] conducted a study on land cover change analysis over Simly dam watershed, ICT; the results derived from maximum likelihood supervised classification showed tree cover loss of up to 26% and 6% increase in settlements from 1992–2012, based on Landsat 5 TM and SPOT-5 imagery, respectively. Similarly, the watershed analysis of Rawal dam, ICT using Landsat 5 TM imagery, showed

3% degradation of tree cover and 2% gain of settlement from 1992–2012 [45]. Another ICT land cover change dynamics study conducted by Hassan et al. [46] utilized 30 m Landsat 5 TM data for 1992 and 2.5 m SPOT-5 data for 2012, using the maximum likelihood algorithm for image classification. The study revealed a decrease in forest cover of approximately 49% and over 213% gain of settlement area from 1992–2012. Sohail et al. [47] conducted a study to assess the water quality index and analyze the major change in land cover types, vegetation cover, rate of urbanization and its possible impact on groundwater resources, vegetation, and barren land. They used Landsat images for the years 1993, 1997, 2002, 2007, 2013, and 2017 for the assessment and mapping of land cover dynamics; according to their findings, from 1993 to 2017, vegetation areas decreased by 101.77 km2, surface water was reduced by 1.10 km2, barren land was reduced by 2.90 km2, while built-up lands expanded by 105.77 km2.

A comparison of Beijing, China and ICT for the role of vegetation in "controlling the eco environmental conditions for sustainable urban environment" was performed by Naeem et al. [42], where they used Gaofen-1 (GF-1) and Landsat-8 Operational Land Imager (OLI) satellite imagery with 8 m and 30 m spatial resolution, respectively. They evaluated various scenarios and models for future development to predict future spatial patterns in both cities. Another study was conducted by Naeem et al. [48] to study the association between green space characteristics, analyzed through landscape metrics, and land surface temperature for sustainable urban environments comparing Beijing, China and ICT.

Khalid et al. [49] conducted a study to quantify the decline of forest reserves and associated temperature variations in a relatively unexplored biodiversity hotspot of ICT, the Margalla Hills National Park (MHNP). In this work, Landsat satellite imagery from 1992, 2000, and 2011 was used to monitor the changes in forest cover and statistical significance tests were used to determine the significance of temperature variation associated with a shift in land cover classes. The study finds that deforestation and forest degradation by local communities is an ongoing practice in MHNP; this necessitates the promotion of conservation practices to minimize ecological disturbances here [49]. Batool and Javaid [50] carried out a study on the assessment of Margalla Hills forest by using Landsat imagery for years 2000 and 2018, and report that the forest cover has decreased from 87% in 2000 to 74% in 2018, whereas built-up area has increased from 5% in 2000 to 7% in 2018, and open land in the study area increased from 2% in 2000 to 7% in 2018. Mannan et al. [51] conducted a study using Landsat imagery, Markov Chain, and Cellular Automata on Margalla Hills, focusing on the quantitative assessment of spatiotemporal land use and land cover changes during 1998, 2008, 2018, and a simulation of 2028. In addition, a forest inventory survey was conducted for biomass and carbon sink estimations. This work shows that the forest area has reduced from 409.36 km<sup>2</sup> to 392.31 km<sup>2</sup> and settlement area has increased from 14.97 km<sup>2</sup> to 39.66 km<sup>2</sup> from 1998 to 2018. The average yearly biomass and carbon losses were 50.34 Gg/ha/yr and 31.33 Gg C/ha/yr, respectively.

The ICT is a relatively new and spatially heterogeneous city surrounded by the Himalaya mountainous dense forest as compared to other fast growing and expanding cities like Dhaka, Bangladesh [31,52–55], New Delhi, India [56], Beijing, China [42,48,57,58], Shanghai, China [59–61], Tokyo, Japan [27,62], etc. Based on the literature review, we observed that most studies of the ICT land cover dynamics have used di fferent remotely sensed data, methods, definitions, and classification schemes, and have provided diverse results. Most studies which have analyzed the land change dynamics in the ICT focus on the overall analysis of land-cover and land-use change, and a detailed analysis of the landscape ecology and urban forestry characteristics is missing. There has further been very little focus in these studies towards urban landscape metrics and indicators of sustainable urban growth such as LCRPGR.

#### *1.2. Study Objectives*

In this paper, well established, proven, and articulated research methodology, satellite datasets, and definitions of features were adopted with the goal to systematically achieve the following defined objectives:


#### **2. Study Area**

The ICT is the capital city of Islamic Republic of Pakistan (Figure 1) located in the Potohar plateau. It comprises an area of 906 km<sup>2</sup> including mountains and uneven plains exceeding 1,175 m in height above the mean sea level [63]. According to the 2017 national population census, the total population of ICT is approximately two million, which makes it the ninth largest city of Pakistan. It has a humid and sub-tropical climate with four distinct seasons: autumn, spring, summer, and winter. The temperatures vary from 13 ◦C in January to 38 ◦C in June. ICT consists of five planning zones: Zone I, II, and V are reserved for planned urban development, while the remaining two zones, III and IV, are managed as National Park and the rural fringes. Marble and chemical factories, steel mills, flour mills, oil units, pigments, paints, and pharmaceutical manufacturing plants are some of the main industries in the city [34].

**Figure 1.** Study area map—Islamabad Capital Territory (ICT), Pakistan. The bounded box inset covering Zone I and Zone II partially is representing the area shown in the Figure in Section 5.

#### **3. Materials and Methods**
