1. Introduction
Global economic growth has been observed in recent years, leading to worry about the effects of globalization on neighboring regions [
1,
2,
3]. Urbanization that takes place on the urban periphery, due to its unpredictable consequences, can affect the sustainability of the area. Several variables are responsible for the rise and development of a major city and its relationship with the Peri-Urban Area (PUA), including economic development, the intensity of trade expansion and the political motivation of the state [
4].
The share of urban population in the world rose from 14% in 1900 to 29.1% in 1950 and 54% in 2014; the global urban population is projected to total 66% by 2050 [
5]. The expansion of urban centers in emerging nations has prompted a shift from compact and interconnected cities to sprawling, uncontrolled urban development. This phenomenon means that metropolitan areas are fraught with many new difficulties that planners, governors and legislators must contend with. The effects of these changes in terms of land use and land cover, as well as urban sprawl, represent a major issue [
6,
7,
8]. Confirmed data on land-pattern changes are necessary to achieve ongoing growth and correctly manage natural properties. These are also essential factors for many environmental functions. Fundamentally, the words comprising Land use land cover are not same, but they are often used in similar and alternative senses. “Land cover” is used to indicate the natural settings of the Earth’s exterior, including the spread of forestry, crop fields, etc. In contrast, land use denotes anthropogenic interventions primarily motivated by economic issues [
9].
Launched in July 1972, the Earth Resource Technology Satellite (ERTS) 1 (later known as Landsat 1) has made a significant contribution to the development of remote sensing applications, including land cover classification [
10,
11]. These developments emerged as a result of improvements to Landsat images and computer technology, the development of geographic information systems (GIS) and the policy of Landsat’s open access. Landsat images have been used to categorize land cover over the last four decades. Land cover refers to the physical materials that cover the Earth’s surface, such as forests, lakes and grasslands. Thus, in order to classify land cover, one must use different classification methods developed in the field of remote sensing. As a result of the Landsat data access policy change in 2008, as well as the development of high-performance computer capabilities, these remote sensing classification methods have been applied to an increasingly wider variety of Landsat images. Recently, Landsat-based land-cover classification methods have become a hot topic, especially given the current effects of climate change [
12]. Fast remote-sensing-based LULC mapping is now available and offers high accuracy at a reduced price [
13]. Remote sensing is a dynamic tool that provides valuable information series covering both time and space in detail [
14]. A global positioning system (GPS) is an important device used to gather field data as reference data to calibrate a classification or in a Geographic Information System for mapping [
15]. The latest developments in geographic information science, remote sensing technology and digitalization, particularly with the advent of ground-breaking machine learning and deep learning algorithms, have created new ways to improve current processes and resolve land-use and land-cover issues. Furthermore, the maximum likelihood classifier (MLC) has been widely used and is now a well-accepted methodology. Several previous studies were conducted in this area [
16,
17,
18].
Diverse factors are responsible for the changing patterns of land use and land cover around the world. Anthropogenic and socio-economic issues have been commonly highlighted as decisive elements in diversification. The petroleum industry and various types of structures are major players that affected the land-use patterns in Sekondi–Takoradi, Ghana [
6], over the last three decades (1986–2016). A study in [
6] also indicated that a shrinkage of crop land would be the ultimate result, possibly leading to poverty. This result would conflict with Sustainable Development Goal (SDG) one (End poverty in all its forms everywhere) as declared by the United Nations. The dominant factors in Shanghai, China [
19], were anthropogenic interventions and socio-economical behaviors. A study in Latvia [
20] found that the areas near roads experienced massive LULC changes, while areas farther from the capital experienced fewer changes. Around the Mekong delta in Vietnam [
21], the primary parameters causing changes to land classes included economic, socio-economic and demographic policies.
Several studies related to remote sensing and GIS have assessed different LULC aspects, focusing, e.g., on forest/vegetation [
22,
23] and agricultural land [
24,
25,
26].
Despite the various perspectives on changing land patterns, in the last few years in developing countries, urbanization has been the primary factor responsible for changes in land use/land cover [
27,
28,
29]. Almost one-half of the world’s population dwells in urban areas. However, the total urban land area, at present, represents only three percent of the world’s land surface [
30,
31]. With increases in urbanization, especially in developing countries, large research studies based on urban areas [
32,
33,
34,
35] have been executed to reflect actual urban scenarios.
GIS and remote-sensing applications in LULC assessments are relatively new in Bangladesh. Few studies have emphasized different land classes such as urban land and agricultural land. Dhaka, the capital of the country, has the highest priority for LULC assessment, as Dhaka is one of the largest and most densely populated cities in the world. LULC change detection of the Dhaka metropolitan city covering approximately half a century (1960–2005) revealed that urbanization squeezed out other land classes [
36]. There was also another approach used to quantify the urban expansion of greater Dhaka [
27]. This approach revealed the parameters responsible for facilitating urbanization, including altitude, demography and economical activities. Aside from these factors, few analyses have been conducted on ecological aspects. Such studies examined a Ramsar site [
37] and a river catchment area [
38].
Bangladesh is quickly moving toward further developments and achieved the status of a lower-middle-income country in 2015, with a GDP growth rate of 8.2% in 2019 [
39]. Recently, the Bangladesh government proclaimed that the rural area would transform into an urban area. As Gazipur is the neighboring district to Dhaka, and this area is experiencing fast-paced development, Gazipur was selected for the current study. The primary objectives of this study were as follows:
- (1)
Quantify and assess the overall land-use/land-cover changes in the Gazipur district, a rapidly developing industrial hub area in Bangladesh, over the period from 1990 to 2020;
- (2)
(Compute the changes in built-up areas in terms of both direction and distance during the time frame of 1990–2020;
- (3)
Determine the major driving factors of land-use/land-cover changes, especially the reasons for the rapid progression of urbanization.
In brief, this research analyzes the land-use changes from the 1990s, when industrialization commenced in Bangladesh, to present. During this time frame, this area experienced urban development and became the largest city corporation in Bangladesh, where significant industrial activities have changed traditional land patterns. Moreover, the expansion of urban area in Bangladesh underscores the need for policy makers to develop foolproof policies to attain SDG 11 (Make cities and human settlements inclusive, resilient and sustainable).
The remainder of this article is formulated as follows. In the second section, the study area is described. In the third section, the materials and methods are presented. In the fourth section, the results are explained, and a discussion is developed. The fifth section is dedicated to urban expansion. In
Section 6, the driving factors are presented. Finally, the conclusions are provided in
Section 7.
7. Conclusions
This study aimed to quantify land-use/land-cover changes and urban spread by using remote sensing and GIS technologies along with multi-temporal Landsat imagery of the Gazipur district, Bangladesh, during 1990 to 2020. This study also uncovered the impact of economic and population growth on changes in the conventional land-use/land-cover arrangement of the area. The overall outcomes of the study indicate that agricultural land experienced maximum modifications over this time period, with a total reduction in agricultural land of 264 km
2. The vegetation and built-up area showed increasing trends, but vegetation land increased due to the homestead planting of trees, which is a regular phenomenon in Bangladesh. This result also suggests that the increasing trend of population is one of the key factors underlying land-use changes. The built-up area grew by approximately five-fold (18 to 104 km
2) during the study period, indicating significant urbanization. Economic growth, location and accessibility were observed to be the most important elements that enhanced the urban area, while agricultural land contributed the most to urban growth. The urban area was largest during 2000 to 2010. In 2020, fallow land became larger (224 km
2), but this was a transitional effect of urbanization. Purbachal, the largest government housing project, was also shown to be fallow in this analysis, due to its provisional change phase. Urban expansion demonstrated spreading through both the active built-up area and the main transport routes, including the Dhaka bypass and the Dhaka–Mymensingh Highway, as commonly found around large Asian cities, including the Chinese capital of Beijing and the Japanese capital of Tokyo [
68,
69]. The results of this study provide a complete description of the area for the stakeholders and policymakers who will realize the Bangladesh government’s plan to transform rural areas into urban areas, thereby facilitating appropriate considerations of environmental issues in respect of sustainable development goal 11.