Startseite Measuring urban growth dynamics: A study in Hue city, Vietnam
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Measuring urban growth dynamics: A study in Hue city, Vietnam

  • Nguyen Tran Tuan EMAIL logo
Veröffentlicht/Copyright: 31. Mai 2024
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Aus der Zeitschrift Open Geosciences Band 16 Heft 1

Abstract

The proliferation of urban sprawl has emerged as a prevalent occurrence in response to the rapid expansion of the worldwide population. The objective of this study is to determine the level of freedom for urban expansion using Pearson’s Chi-square (χ2) index and the amount of urban spatial dispersion using the Shannon entropy ( H n ) index in Hue City, Vietnam. While the first index asserts the degree of freedom for observed urban growth above expected urban growth, the second index is applied to analyze urban spatial dispersion patterns through richness and evenness. Land use map data were collected from Japan’s JAXA agency from 1990–2020. Research results show that, in the past 10 years (2010–2020), the level of urban freedom expansion in Hue city has increased compared to the period 2000–2010 and 1990–2000, with corresponding χ2 values of 219.18, 150.05, and 106.95. The South-Southwest (TNN) area is also the area with the most significant urban freedom expansion among the eight regions in Hue City through three stages. The urban dispersion level in Hue City has also expanded recently when the H n and H n ′ indexes gradually increased from 1990 to 2020. This result puts pressure on the Hue city government on how to develop the city sustainably. Therefore, the study also points out some disarmament to contribute to sustainable urban development.

Keywords: urban expansion; sustainable urban development; entropy; urban sprawl; land use change

1 Introduction

Socio-economic development in each country always requires improving the quality of life, raising per capita income, and significantly improving the quality of urbanization [1,2]. Increasing the rate of urbanization means a shift of labor from rural areas to cities [3]. Therefore, this shift helps people have the opportunity to find jobs with higher incomes [4]. This promotes production and consumption activities. Ultimately, it promotes the country’s economic growth. When a country has a high rate of urbanization, it is often a country with high-income levels, high spending levels, and developed manufacturing activities [5]. On the contrary, a country with a low urbanization rate often has lower income levels, where production and consumption activities are not developed [3].

Over the past five decades, Asia has undergone enormous demographic changes [6]. One of the most notable transformations observed in recent times is the migration of individuals from rural areas to urban centers [7]. The urbanization rate in Asia, as a proportion of the overall population, is seeing significant growth. In the year 1950, the urban population in Asia amounted to approximately 232 million individuals, constituting approximately 17% of the total population of the continent [8]. In the year 2005, the urban population of Asia experienced a significant increase, reaching a total of 1.6 billion individuals, which accounted for almost 40% of the overall population in the region [9]. There is little doubt that the Asian region will experience ongoing development, leading to a notable rise in urbanization levels. Based on projections provided by the United Nations, it is anticipated that by the year 2025, over 50% of Asia’s population will reside in urban regions, with a growth rate of 2.5% annually [10]. It is projected that by the year 2030, around 54.5% of the people in Asia will have transitioned to urban areas. By the year 2030, it is projected that Asia would be home to half of the urban population, indicating that one out of every two urban dwellers will reside in this region [10].

Because of the tremendous increase in the number of people living in the world, urban sprawl is becoming a regular phenomenon [11]. Many researchers have pointed out the impacts of urban sprawl in different directions. Sociologists believe that urban sprawl has increased inequality between urban and suburban residents [12,13]. It can refer to the impact of land use separate from public distribution and services such as schools, medical facilities, and entertainment [14]. In this context, social vulnerability can be observed in suburban areas lacking adequate social infrastructure [15]. Economists suggest that urban sprawl is responsible for decreasing agricultural land values in places where growth occurs [16,17]. Urban expansion will determine higher transportation costs due to increased daily commuting [18,19]. Land conversion related to suburban development and changes in land use also cause real estate prices to increase rapidly [20,21]. On the other hand, biologists say that urban expansion has had a negative impact on biodiversity, thereby degrading the natural habitats of some species [22,23,24]. Urban sprawl is also linked to energy, air pollution, and climate change issues [25,26]. Changes in land cover also led to reduced CO2 uptake due to vegetation removal [27,28]. Several studies have used indicators to measure urban sprawl. Landscape expansion index is an index that analyzes urban growth types, in which each new urban segment is classified into one of the recognized growth types. This index provides a deeper understanding of landscape change processes [29]. Another study by Santos et al. [30] determined the change based on two indicators: Building density and built volume density. These indicators consider changes in the number and volume of new and demolished buildings. The building density index was also used in the study by Zhu et al. [31]. Another index that has been applied by many studies is Shannon [32,33].

In Vietnam, urban development and growth are slower than in some countries in the region and are characterized by rural urbanization [34], where villages and agricultural communities were transformed into districts and wards [35]. Urban development is uneven across regions, with delta and coastal areas developing faster than mountainous and highland areas [36]. In big cities, there is a tendency to expand space from the urban core to the periphery. However, in Vietnam, there have not been many studies to measure urban growth, so the two research questions that this study will attempt to address are:

– What is the degree of freedom for urban growth in Vietnam?

– How dispersed are the urban areas in the research area?

The research has already been conducted in Hue (the ancient capital of Vietnam from 1802 to 1945). In Section 2, the study will give an overview of the data on the urban sprawl situation in some countries around the world, followed by the research method of this article Section 3. In the research direction, the author briefly introduces the geographical location of the research area and the indicators that need to be calculated in this study. They consist of the Shannon entropy ( H n ) and Pearson’s Chi-square (χ2). Additionally, this is the first study in Vietnam to use these two variables to quantify the degree of free urban expansion and spatial dispersion. Then, the research results are presented in Section 4. Finally, there is the discussion (Section 5) and conclusion (Section 6), where the author will point out suggestions to improve problems in the research area. The results of this research will contribute to supplementing the available scientific literature and help readers have another case for reference. This study can also be considered a reference for planners in Vietnam to solve the ongoing urban expansion problems in Vietnam, especially for urban areas with the same classification as Hue city.

2 Urban sprawl in some countries around the world: Literature review

Urban sprawl impacts land management and use in different countries and regions differently due to different levels of urban expansion, urbanization rates, and natural, economic, and social conditions [37,38]. From 1990 to 2014, urban land grew by 95.2%, equivalent to a growth rate of 4% per year. Europe witnessed the most vigorous urban expansion, with 51%, followed by Southeast Asia and West Africa [39]. By 2030, it is anticipated that this expansion will add 1.2 million km2 of new urban land. The worldwide urban land area, in 2000, was tripled by this increase [40]. Meanwhile, the highest urban expansion per capita rates occur in Oceania and North America. In contrast, South America observed a slight decrease in urban expansion rates [39]. In addition, the density in cities in developing countries was about three times higher than in developed industrial cities. Density in all regions is also decreasing over time. Therefore, if the average density continues to decrease at a rate of 1.7% annually, the construction area in developing countries will increase from 200,000 km2 to over 600,000 km2 after 30 years (2000–2030) [41].

Also, according to the study of Behnisch et al. [39], the growth in urban development rates was greatest in several European countries such as the Netherlands, Portugal, Luxemburg, and Germany. These countries are followed by several countries in Asia, America, and Africa, such as Japan, China, South Africa, and the United States. In particular, China and many other Asian countries are expected to face much pressure from urban land expansion after the 2050s [42]. Therefore, in the following paragraphs, the author will review the current situation of urban land expansion in some of the above countries. Furthermore, an estimated 1.8–2.4% of the world’s arable land area would be lost by 2030 due to urban land expansion, which also resulted in a loss of 3–4% of worldwide agricultural production in 2000. In particular, Asia and Africa will lose up to 80% of arable land due to urban expansion. This urban expansion will likely continue on productive cropland at a rate of 1.77 times the global average [43]. By 2050, about 50–63% of urban land is forecast to expand on current cropland [42].

China is one of the countries with relatively large urban growth in the world [39]. Since the beginning of the opening-up and reform policy in the late 1970s, China’s urbanization level has increased rapidly from 17.4 to 60.6% between 1970 and 2019. This level is forecast to grow to 72% in 2030 and over 90% in 2050 [44]. The strong wave of urban expansion in China began in the 2000s [45]. Many studies have also shown that urbanization has increased the urban construction land area nearly five times, with an average annual rate of 8.1% from 1992 to 2015 [46]. It contributed to a severe decline in arable land area, specifically arable land area decreased by 4.1 million hectares (1996–2002), corresponding to 3.6% per year [47]. Besides, the loss rate of arable land in the Eastern region of China in 1995–2000 and 2000–2008 was 7 and 29.2%, respectively [48]. However, in the first two decades of the twenty-first century, although agricultural land in China still tends to decrease, the speed gradually decreases. Specifically, in 2000–2010, it is 2.91%, and in 2010–2020, it is 0.41% [49]. To have land for urban development purposes, China also implemented mandatory land acquisition for 30% of households (with a portion of land) in 2018 [50]. 53,700 km2 is the agricultural land in China occupied for urban expansion from 1980–2020 [45]. Therefore, to limit the decline in arable land and increase labor productivity due to the impact of urban expansion, the Chinese government has introduced many policies, such as the State protecting and strictly controlling the conversion of farmland to other purposes.

The urban sprawl wave originated in the USA from migration to the suburbs in the 1950s [51]. According to Habibi and Asadi [12], land consumption in urban areas with more than one million inhabitants rose from 161 to 243 and 293 sq mi per 1,000 people over the years 1950, 1970, and 1990, respectively. The land available for urban and construction purposes increased by 34% from 1986 to 1997 [52]. It is projected that urban land in the USA will rise from 3.1 to 8.1% in 2000–2050, covering a total area of 392,400 km2. This region covers an area that is considerably greater than Montana. Additionally, it is anticipated that Massachusetts (61%), Rhode Island (70.5%), Connecticut (60.9%), and New Jersey (63.6%) will each have more than half of the total urban land. Over a broader period, between 1 and 1.4 million acres (4046.856 and 5665.599 km2) of urban land will be added per year in the United States of America between the years 1997 and 2060 [53]. Urban land expansion also reduces the area of other types of land, in which the area of forest land and cropland is often changed. Forest resources are expected to lose 26 million acres (105218.3 km2) to urban uses and development by 2030 [54]. Agricultural land was converted to urban land by 16.3 million acres (65963.76 km2) from 1992 to 2002. In the next 10 years (2002–2012), this number was nearly 15 million acres (60702.85 km2). What is alarming is that this loss includes 11 million acres (44515.42 km2) of prime land for intensive food and crop production [55]. One forecast is that from 2016 to 2040, 18.4 million acres (74462.16 km2) of agricultural land will be converted. Of these, around 6.2 million acres (25,090 km2) of agricultural land would have been transformed into urban land, including industrial parks, commercial structures, and high-density residential areas; 12.2 million acres (49371.65 km2) are devoted to low-density residential areas [56].

With approximately 78% of its population residing in urban areas, Germany, a country situated in Central Europe, has also experienced an urban expansion over the last quarter-century [57]. Within 50 years (1950–2000), urban land in the West German region nearly doubled from 7 to 13.8% [58]. Besides, agricultural land use has continuously decreased since unification, and agriculture seems to be increasingly squeezed by different land use needs, including urban expansion [59]. In 2002, Germany proposed a strategy called “Perspectives for Germany,” and one of the goals of this strategy was to reduce the rate of conversion of non-urban land use to urban land from 130 hectares to 30 hectares per day from 2000–2020 [60]. However, this goal does not seem to be effective, and it is said that there should be a clear delineation between state goals [61]. The German Farmers’ Association reports that the loss of agricultural land is still about 70 ha per day [59]. In Nürnberg (Nuremberg), within 15 years, from 2004 to 2018, 2.7% of agricultural land was converted for settlement and infrastructure development [62]. In Berlin, the population is expected to increase by 180,000 people from 2017 to 2030 and reach 3.96 million in 2040. To address the housing demand of approximately 27% in 2030, the Berlin city government intends to construct 16 new urban areas comprising nearly 52,000 residences [63].

As seen by the literature review above, land use patterns are altered by urbanization and expansion in every nation. In particular, the land area converted for infrastructure and urban development comes mainly from agricultural land. Consequently, the depletion of arable land may give rise to additional sustainability hazards and jeopardize the livelihoods of individuals; thus, effective policies are required to regulate urban expansion. In other words, gradually reducing agricultural land area is a significant challenge for food security at local, regional, and global scales. Thus, it is necessary to change how land is used to limit the conversion of land use purposes and apply effective land use methods.

3 Materials and methodology

3.1 Study area

Vietnam has a favorable geographical location, located in the center of Southeast Asia, a bridge between East and West cultures. With a long coastline and many bays and islands, Vietnam has great potential to develop the marine economy. In addition, Vietnam also has many rivers and lakes creating beautiful natural landscapes and is rich in natural resources. Vietnam’s terrain is diverse, including plains, hills, and plateaus. The Red River Delta is the largest delta in Vietnam, accounting for about 1/3 of the country’s land area. Mountains account for about ¾ of Vietnam’s land area, distributed in the north, west, and south. The plateau accounts for about 1/10 of Vietnam’s land area, concentrated in the north and south. In addition, Vietnam also has many diverse ecosystems, from tropical forests, temperate forests, deserts to swamps and deltas. This has created a rich habitat for animals and plants and is also an attraction for nature-loving tourists.

Located in the center of the country and the central critical economic region, Hue City has been identified by the Central Government as a class I urban area under Thua Thien Hue province (Figure 1). Hue is one of 19 class I urban areas directly under the province in Vietnam. It is oriented to develop into a grade I urban area directly under the central government by 2045. Therefore, this research conducted in Hue is also seen as scientific evidence for planners with orientation in development, and is an example for other large cities on the path to urban upgrading. This city is also located in the north-south axis and near the East-West corridor of the Trans-Asia route. Therefore, Hue has much potential for development in economic and trade exchange. In particular, Hue City is the last feudal capital of Vietnam, which has a long history and cultural tradition with unique values and identity. Therefore, Hue is determined to be the place to preserve, conserve, and develop the heritage, monuments, and culture of Thua Thien Hue province. Besides, the city converges terrain types such as hills, plains, rivers, and lakes. This creates an ideal natural-urban-cultural landscape space to organize various festivals and sports tourism activities. In other words, Hue is known as the festival city of Vietnam. Therefore, Hue City is in a location with rich and diverse natural conditions and ecosystems.

Figure 1 The geographical location of Hue city. Source: author’s compilation, 2023.
Figure 1

The geographical location of Hue city. Source: author’s compilation, 2023.

3.2 Data sources and image classification

The map data source is collected from the Japan Aerospace Exploration Agency or JAXA. This agency was established by merging three institutes: ISAS, NAL, and NASDA. From this merger, JAXA was designated as the agency supporting the use and development of aerospace by the Japanese government. The aim of this study is to compare changes in urban space expansion over each period, so map data were collected every 10 years (from 1990 to 2020). Field surveys and visually interpreted data independently verified these map data. These maps have a spatial resolution of 30 m, and the accuracy is 86, 87, 89, and 92% from 1990 to 2020, respectively. In particular, the closer the data are to the present, the greater the map accuracy. JAXA created the annual land use land cover (LULC) dataset in Vietnam using a random-forest-based algorithm and multiple information-rich geospatial data sources, including Landsat and Sentinel-1 and 2 imagery. Urban land area data are also collected from map data interpretation through QGIS software. The author also uses this software to divide geographical regions of Hue city through the “split with lines” function. This division is shown in Figure 2 with eight partitions. They include DBB (North-Northeast), DDB (East-Northeast), DDN (East-Southeast), DNN (South-Southeast), TNN (South-Southwest), TTN (West-Southwest), TTB (West-Northwest), and TBB (North-Northwest).

Figure 2 Research zoning in Hue city. Source: author’s compilation, 2023.
Figure 2

Research zoning in Hue city. Source: author’s compilation, 2023.

3.3 Some calculation formulas

In order to assess the degree of freedom for urban growth, the author makes use of the Chi-square statistical indicator developed by Pearson. The Chi-square test is a statistical test used to compare the observed and expected results between categorical variables in a population of data. The purpose of testing is to determine whether the difference between observed data and expected data is due to chance or due to the relationship between the variables under study. In this research, this measure provides further evidence that the observed urban growth is significantly higher than the projected growth. This index has been described by Almeida et al. [64] through the below formula:

(1) χ 2 = ∑ i = 1 n ( M ij − M ij E ) 2 M ij E ,

where χ2 is the Pearson’s Chi-square statistical index; M ij represents the urban land area appearing in column i; M ij E is the forecast index of urban land area in column i. M ij is calculated through QGIS software with the data source shown in Section 3.2, while M ij E is calculated through the following formula:

(2) M ij E = M i s × M j s M g ,

where M j s is the total amount of urban land in the row; M i s is the total amount of urban land in the column; and M g is the sum of urban land areas in rows and columns.

The study also calculated an index called Shannon’s entropy. German scientist Clausius (1870) first proposed the idea of entropy in thermodynamics study as a way to quantify a system’s level of disorder. Later, entropy was used in many fields, from technology to sociology and economics. Primarily, in 1948, Shannon used this index to indicate the average information content of a message, and it was used in encoding communication signals. From then on, this index was called Shannon’s entropy. Shannon’s entropy measure was used to study the density and distribution of metropolitan areas. The ratio of land cover classes is a measure of evenness, whereas the number of classes or objects researched is a measure of richness (in this study, urban land is the object studied). This index is also widely accepted in assessing urban growth [65,66,67]. It is calculated using the below formula:

(3) H n = − ∑ i = 1 n P i log e ( P i ) = ∑ i = 1 n P i log e 1 P i .

In which, P i is the ratio of urban land in the ith area. It is determined by the formula

(4) P i = X i ∑ i = 1 n X i ,

where X i is the value of the urban land area in the ith area.

Henry Theil (1972) coined the term “relative entropy” to describe his version of the Shannon entropy index for measuring spatial dispersion [33]. This idea stands in for the greatest possible dispersion, in which the variable is dispersed across many different types of groups or geographic areas. If the relative entropy value equals 1, it indicates an even distribution over all regions. If that value is 0, the distribution is only concentrated in certain regions. The following formula expresses this index:

(5) H n ′ = ∑ 1 n P i log e ( 1 P i ) log e n .

4 Research results

4.1 LULC in Hue city from 1990 to 2020

Table 1 shows the LULC categories in hectares in Hue City from 1990 to 2020. The classification results show that urban land area has grown enormously over each period, with an increasing solid trend in the past 10 years with over 16%. This increase is nearly three times higher than that during 1990–2000 and 1.5 times higher than that during 2000–2010. Statistical data also show that the area of agricultural land tends to decrease sharply, especially the cropland type. This type of land accounted for nearly half of Hue City’s area in 1990, but by 2020, this number is only nearly 25%. Meanwhile, the area of rice cultivation land tends to decrease slightly, with only 571.77 ha, equivalent to 7.22% after 30 years. A reduction equivalent to rice land is forest land, with 6.2%. However, the conversion trends of these two types of land are different. While rice land area appeared to have remained unchanged between 1990 and 2000, forest land area saw nearly 5% growth in the first study period. A rapidly decreasing trend followed this in both types of land.

Table 1

Statistics of LULC classes in Hue city

Categories 1990 2000 2010 2020
Area (ha) % Area (ha) % Area (ha) % Area (ha) %
Urban land 820.08 10.35 1202.49 15.17 1989.99 25.11 3273.84 41.31
Paddies land 1801.17 22.73 1794.15 22.64 1435.14 18.11 1229.4 15.51
Crop land 3653.28 46.10 2843.82 35.88 2359.89 29.78 1936.53 24.43
Grass land and scrub 19.71 0.25 65.25 0.82 406.53 5.13 241.29 3.04
Barren land 52.02 0.66 27.54 0.35 104.85 1.32 55.8 0.70
Forest 1193.85 15.06 1585.17 20.00 1215.36 15.34 702.18 8.86
Water 385.2 4.86 406.89 5.13 413.55 5.22 486.27 6.14
Total 7925.31 100 7925.31 100 7925.31 100 7925.31 100

Source: author’s calculation, 2023.

4.2 Degree of freedom for urban growth in Hue city

The research first illustrates changes in urban land usage in several Hue city regions before calculating the degree of freedom for urban expansion (Figure 3). The rate of change in urban use in the three periods is completely different among the eight regions, and this index has been increasing in recent times. While the highest rates of change were witnessed in the period 1990–2000 and 2000–2010 at nearly 89 and 127%, respectively, in the Central region, the level of change in the period 2010–2020 is highest in TNN with over 450%. Besides, if growth between urban areas is compared, the city’s northern area has developed more slowly than the remaining areas in the past 20 years. In contrast, 1990–2000 will be the city’s southwest area. In addition, zone DNN witnessed the most sustainable growth among the eight regions, consistently ranking second or third during that transition period. Details of the change in the urban land area can be seen more clearly in Table 2.

Figure 3 Urban land use change by the zones in Hue city. Source: author’s compilation, 2023.
Figure 3

Urban land use change by the zones in Hue city. Source: author’s compilation, 2023.

Table 2

Some indicators of urban land in Hue city

Categories Periods Zone (Unit: ha) Total
DBB TBB DDB TTB DDN DNN TTN TNN
Observed urban land 1990–2000 64.89 77.76 68.31 52.83 59.58 48.6 6.21 4.14 382.32
2000–2010 112.32 137.34 158.4 142.38 89.37 88.11 43.83 15.75 787.5
2010–2020 47.52 82.35 159.75 108.99 184.95 334.53 184.95 180.81 1283.85
Total 224.73 297.45 386.46 304.2 333.9 471.24 234.99 200.7 2453.76
Expected urban land 1990–2000 35.02 46.36 60.24 47.41 52.04 73.44 36.62 31.28 —
2000–2010 72.12 95.46 124.06 97.63 107.16 151.24 75.42 64.41 —
2010–2020 117.58 155.63 202.25 159.16 174.70 246.56 122.95 105.01 —
Differences 1990–2000 29.87 31.49 8.07 5.51 7.54 −24.93 −30.32 −27.23 —
2000–2010 40.29 41.88 34.34 44.66 −17.79 −63.13 −31.68 −48.57 —
2010–2020 −70.15 −73.37 −42.41 −50.17 10.25 88.06 62.00 75.80 —

Note: The sum column of the two categories (expected urban land and differences) does not need to be calculated. Source: author’s calculation, 2023.

In addition to showing the observed urban land change data in the three periods, Table 2 also shows the predictions of this change through formula (2). The results show that the urban land area is expected to increase gradually over each period. Additionally, contrary to what has been observed, zone DNN is the area with the most remarkable change in all three research periods, with 73.44, 151.24, and 246.56 ha, respectively. The discrepancy between actual and predicted growth is also displayed in Table 2. Positive values imply more growth than anticipated, whereas negative values suggest slower growth. In particular, 1990–2000 witnessed three areas: DNN, TTN, and TNN. In 2000–2010, there were four regions: DDN, DNN, TTN, and TNN. 2010–2020 also witnessed lower-than-expected growth in four regions: DBB, TBB, DDB, and TTB.

Table 3 calculates Chi-square statistics through formula (1), and the lower limit of the χ2 index is 0. The results of Table 3 show that the degree of freedom for urban growth differs between regions and periods. During 1990–2000, the degrees of freedom did not show much difference in zones DBB, TBB, TTN, and TNN. When an observation is made in zone TTB and χ2 = 0.64, it indicates that the observed value for this time period is fairly close to the expected value. In 2000–2010, the degree of freedom for urban expansion showed a more apparent difference than in the previous period. TNN has the highest χ2 index at 36.63, while DDN has the lowest χ2 index with only 2.95. These zones had the highest and lowest χ2 index in 2010–2020, with 54.72 and 0.60, respectively. However, a higher degree of freedom does not mean an area has a higher urban sprawl.

Table 3

χ 2 values by zones and periods in Hue city

Periods Zone χ 2
DBB TBB DDB TTB DDN DNN TTN TNN
1990–2000 25.47 21.40 1.08 0.64 1.09 8.46 25.11 23.70 106.95
2000–2010 22.50 18.37 9.51 20.43 2.95 26.35 13.30 36.63 150.05
2010–2020 41.85 34.59 8.89 15.82 0.60 31.45 31.26 54.72 219.18

Source: author’s calculation, 2023.

4.3 Level of urban spatial dispersion in Hue city

The variable in this study is urban land, and Shannon’s entropy is utilized to quantify the extent of spatial compression or dispersion of the variable between places. Shannon’s entropy and relative entropy are calculated using formulas (3) and (5), respectively. The H n value ranges from 0 to log e n . If H n = 0 , then there is a maximally concentrated distribution within a region, and if H n = log e n , then there is an evenly dispersed distribution across regions. This means that if H n is more prominent in its value range, the area has a wider dispersion of urban land. In this study, log e n = 2.079, so the H n values in Table 4 meet the requirements. Meanwhile, H n ′ varies from 0 to 1. Compact or dense urban areas are indicated by values near 0, whereas distributed urban sprawl is indicated by values near 1. Therefore, higher H n ′ means higher extension. Besides, the threshold value of H n ′ = 0.5 . If the value is less than 0.5, it is considered a concentrated distribution of urban areas, while H n ′ > 0.5 is considered urban expansion. In addition, to calculate the urban growth rate in each area and direction, the author calculated the change in H n ′ in three periods in Hue City. If the ∆ H n ′ value is positive and higher, it indicates higher urban expansion, and if ∆ H n ′ is negative, it indicates a more compact and concentrated urban area. The results of these indicators are shown in Table 4.

Table 4

Shannon entropy and relative entropy

Zones Shannon entropy Relative entropy Change in relative entropy
1990 2000 2010 2020 1990 2000 2010 2020 1990–2000 2000–2010 2010–2020
DBB 0.310 0.307 0.297 0.248 0.149 0.148 0.143 0.119 −0.001 −0.005 −0.023
TBB 0.314 0.318 0.313 0.273 0.151 0.153 0.150 0.131 0.002 −0.002 −0.019
DDB 0.329 0.323 0.323 0.302 0.158 0.155 0.155 0.145 −0.003 0.000 −0.010
TTB 0.190 0.221 0.263 0.244 0.091 0.106 0.127 0.117 0.015 0.020 −0.009
DDN 0.320 0.312 0.290 0.286 0.154 0.150 0.139 0.137 −0.004 −0.010 −0.002
DNN 0.194 0.219 0.230 0.295 0.093 0.105 0.110 0.142 0.012 0.005 0.032
TTN 0.145 0.124 0.139 0.208 0.070 0.059 0.067 0.100 −0.010 0.007 0.033
TNN 0.091 0.079 0.079 0.182 0.044 0.038 0.038 0.087 −0.006 0.000 0.050
Total 1.894 1.902 1.932 2.038 0.911 0.915 0.929 0.980 — — —

Source: author’s calculation, 2023.

Results from Table 4 show that the value of H n tends to increase over 4 years. This means that urban areas are increasingly expanding in Hue City. While urban free expansion appeared more in DBB, TBB, DDB, and DDN areas in 1990, 2000, and 2010, this level was similar in almost eight zones of the study area in 2020. The H n ′ index also increases yearly and is more significant than 0.5, meaning Hue City has urban expansion with increasingly wider dispersion. With ∆ H n ′ = 0.015 , the TTB zone shows the highest urban expansion in 1990–2000, and the lowest is the TTN area with a value of −0.01. During 2000–2010, zone TTB also showed the highest and lowest urban expansion in DDN. Meanwhile, zones DNN, TTN, and TNN witnessed high and scattered urban expansion in 2010–2020 and the lowest in zone DBB.

5 Discussion

Urban areas are always considered places that hold essential political and economic power in society and have a strong influence, greatly influencing the region’s and country’s socio-economic development. The economic contribution of urban areas is enormous. Urban areas are often centers and driving forces for the country’s and region’s economic development. Urban areas contribute to GDP, industrial service, and economic growth values. Therefore, urban expansion needs to be accompanied by sustainable urban development.

The results of this study show that land use conversion between land groups in Hue city is similar to most places in the world. Similar to the results of previous studies, the urban land area in Hue city increased with a growth rate of 30% in 30 years. The area of land planted with rice and other crops tends to decrease as in China, the United States, and Germany. Forest land in Hue City shows a decline similar to the expected decline in this type of land in the United States. These results show that the trend of changing land use purposes in Hue city, Vietnam is in the same flow as the changing land use purposes in the world.

The results of urban expansion at the research location also show that the development between regions within an urban area is not in harmony with each other. This is also the general situation of urban development in Vietnam. The pace of urbanization is fast, but the quality is not high. The general trend of urban areas in Vietnam today is to develop rapidly, expanding both in land area and population. Among them, the two main urbanization trends are (1) the inner-city area develops strongly into the peripheral areas, leading to the adjustment of urban boundaries; (2) Urban expansion is based on merging administrative units within the boundaries of a province. Most rapidly urbanizing areas have underdeveloped infrastructure systems, low educational levels, and small-scale economic forms. People here still maintain their culture and live according to rural traditions, not being able to adapt to urban life. Therefore, the research results contribute to creating a different perspective for policy makers and planners to implement planning on how to balance and harmonize regions of a locality.

Although the study has addressed the research questions raised, there are still some limitations that need to be overcome and maybe directions for future research. This is the first study in Vietnam to use two indices, Pearson’s Chi-square, and Shannon entropy, to measure the expansion of urban space. However, the research was only conducted in one locality in Vietnam, so it is difficult to show general results on this issue in Vietnam. Along with that, the new study mentions the impacts of urban expansion on land management and use but does not analyze these impacts in depth. Future studies could measure the environmental, social, and economic impacts of urban expansion as well as other issues that urban expansion brings.

6 Conclusion

The goal of this study is to use Pearson’s Chi-square index to calculate the degree of freedom for urban expansion and the Shannon entropy index to determine the degree of urban spatial dispersion in Hue City. The study’s findings indicate that Hue City’s degree of urban freedom growth has grown during the last 10 years (2010–2020) in comparison to the years 2000–2010 and 1990–2000, with matching χ2 values of 219.18, 150.05, and 106.95. Out of the eight zones in Hue City, the TNN area has experienced the greatest expansion of urban freedom over three stages. Furthermore, Hue City’s urban dispersion level has grown recently as a result of the indices H n and H n ′ progressively rising between 1990 and 2020. The Hue municipal government is under pressure to develop the city in a sustainable manner as a result of this outcome. These are also concerns that previous studies have pointed out when studying urban growth levels in some parts of the world. Thus, in order to guarantee sustainable development in this metropolis, the author suggests a few measures.

First, strictly controlling the urbanization process in urban construction planning is necessary. In order to do this, urban construction planning needs to be placed in a comprehensive coordination system with many sectors and Hue city authorities to ensure the feasibility of the planning. Hue City aims to become a centrally run Class I urban area by 2025 (Vietnam only has five centrally run Class I urban areas [68]). Therefore, the city aims to plan and develop based on cultural, historical, and tourism resources. The city is determined to promote an in-depth economic growth model based on exploiting strengths and prioritizing the development of service and tourism industries based on promoting heritage values. To achieve this, the Thua Thien Hue provincial government has planned to invest about 150,000 billion VND (equivalent to about 6.15 billion USD) in infrastructure from 2021–2030 [69]. To achieve this goal, in planning, it is necessary to express the appropriate philosophy of industrialization and urbanization in Vietnam in general and Hue city in particular. Following the planning, the approval of investment projects is the process of implementing the planning. This process must still adopt the idea of only accepting projects that integrate private interests with national interests in industrialization and urbanization.

Second, enhancing urban quality and restoring order in land and urban management is necessary. The fact that many projects in the implementation process are being adjusted has disrupted the planning. In Thua Thien Hue province, 79 projects are behind schedule, and authorities have revoked 35 projects. In particular, some areas considered “golden land” (located in the central area) are abandoned and unused [70]. Therefore, the Hue city government needs strong measures to create changes in land management issues. Along with that, it is necessary to check and monitor the planning implementation process, avoiding the existence of “hanging” and abandoned projects like today. Gradually erasing images of abandoned projects will bring the land to proper use and restore order in land management. Besides, the most sensitive area that needs attention in management is the urban fringe area. All violations regarding land use and implementation of investment projects also occur in this area. Regarding planning, there should be requirements for zoning urban areas, industrial areas, business and service areas, and the peripheries of the above-mentioned areas. In terms of law, it is necessary to assign tasks to the grassroots level to directly manage all land use activities in this peripheral area.

Appendices

Acknowledgements

To complete this research, the author sincerely thanks Dr. Bui Dang Hung, lecturer at Industrial University of Ho Chi Minh City, who guided the author in making maps using QGIS software.

  1. Funding information: Author states no funding involved.

  2. Author contribution: The author confirms the sole responsibility for the conception of the study, presented results and manuscript preparation.

  3. Conflict of interest: Author states no conflict of interest.

  4. Data availability statement: All data generated or analyzed during this study are included in this published article and its supplementary information files.

Appendix

Figure A1 LULC in Hue city from 1990–2020. Source: author’s compilation, 2023.
Figure A1

LULC in Hue city from 1990–2020. Source: author’s compilation, 2023.

Table A1

Urban land areas in eight zones in Hue city

Unit: ha1990200020102020
DBB149.22214.11326.43373.95
TBB154.26232.02369.36451.71
DDB174.60242.91401.31561.06
TTB59.40112.23254.61363.60
DDN162.00221.58310.95459.90
DNN61.29109.89198.00532.53
TTN39.0645.2789.10274.05
TNN20.0724.2139.96220.77
Total819.901202.221989.723273.57

Source: author’s calculation, 2023.

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Received: 2023-11-04
Revised: 2024-04-05
Accepted: 2024-04-13
Published Online: 2024-05-31

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0640
Schlagwörter für diesen Artikel
urban expansion; sustainable urban development; entropy; urban sprawl; land use change
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Artikel in diesem Heft

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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