Home Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
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Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City

  • Ying Han , Jianfeng Zhu EMAIL logo , Donglan Wei and Fangxiong Wang
Published/Copyright: November 12, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

Coastal zones have intense human activity and are ecologically fragile. Assessing ecological risks in coastal zones is crucial to promoting ecological safety and sustainable development for coastal cities. However, the relationship between ecological risk assessment and distance from the coastline is indeterminate in current research. To solve this problem, this study constructs the sea–land gradient and reconstructs the landscape pattern index and landscape ecological risk index (ERI) considering the sea–land gradient paradigm. This study aims to analyze the spatial–temporal variation characteristics and effects of the sea–land gradient on landscape pattern and ecological risk in Dalian City from 2000 to 2015. The results show the following: (1) between 2000 and 2015, a significant effect of coastline on the coastal landscape pattern in Dalian is reducing landscape fragmentation. The extent of this influence gradually diminished along the sea–land gradient. The landscape aggregation of urban building sites near the sea was relatively high and gradually increased over the years; (2) in Dalian, the landscape ERI has decreased near the sea and increased near the inland year by year; (3) the dominant pattern of landscape ecological risk was characterized by a transition from high to low levels within the [0, 2.5] km zone, while a transition from low to high levels predominated within the [2.5, Max] km zone; and (4) the northern region in Dalian primarily goes from high to low risk in the buffer zone near the landside, whereas the southern area has this changing pattern in the buffer zone near the seaside.

Keywords: coastal zone; sea–land gradient; landscape pattern; landscape ecological risk; Dalian City

1 Introduction

The coastal zone is a complex ecosystem composed of humans and nature. The interactions between land and ocean make an obvious impact on urban forms, functions, and potentials [1]. In the context of global warming and sea-level rise, the coastal zone is experiencing the most dramatic changes in resource exploitation, human activities, and the ecological environment. It takes a high degree of dynamism, complexity, and diversity [1,2,3]. With the development of coastal zones and the increasing pressure on resources and environment, there are a lot of ecological and environmental problems because of the susceptibility of coastal areas and human activities. These problems also have a detrimental influence on human society [4]. Therefore, investigations into the spatial–temporal evolution of coastal zones have a significant influence on the sustainable development of coastal cities.

The spatial and temporal evolution of the ecological environment is reflected in changes in landscape patterns. Landscape pattern, which is the result of the interaction of numerous complex physical, biological, and social forces, reflects the geographical distribution and structure of landscape features. It dominates the distribution forms and components of resources and the environment and restricts various landscape ecological processes [5]. The landscape pattern index is the primary instrument for quantitatively analyzing landscape patterns and dynamics, and it is crucial in the study of urban ecology and urban landscapes [6]. In recent years, landscape pattern analysis has been widely used in the study of urban morphology. Utilizing the landscape pattern index to conduct quantitative research on urban landscape [7] serves as a reference for urban landscape structure [8], urban ecological processes [9], and urban expansion research [10]. At present, most studies set up buffer zones along the city center to study the spatial features of urban landscape patterns [11,12,13]. The study region is largely centered in arid and semi-arid areas, deserts, agricultural and pastoral areas, and urban–rural interwoven zones in China.

With the rapid development of urbanization, the interaction between natural and social systems has become increasingly significant, resulting in significant spatial and temporal changes in the landscape pattern. The originally intact and homogeneous natural ecosystems are gradually transforming into complex and heterogeneous patterns, resulting in numerous fragmented and discontinuous mixed patches. Consequently, the ecological and environmental issues arising from these changes have attracted growing attention. Landscape ecological risks stem from the negative effects produced by the interactions between landscape patterns and ecological processes. These negative effects primarily manifest as impacts from human activities or natural disasters on the components, structure, and functions of landscapes. The ecological risk pattern of the coastal zone is a dynamic process, which is characterized by constant updating and change. Ecological risk assessment from the perspective of landscape patterns is rapidly becoming a vital method for investigating and treating ecological problems [14,15,16]. Currently, many scholars both domestically and internationally have combined landscape ecology with gradient analysis methods to study the gradient changes in landscape bands. Research primarily focuses on topographical and elevation gradients [17,18,19], revealing the dynamic changes in landscape ecological risks under various topographical and elevation gradients. It also explores the distribution differences of ecological risks at different levels and their transformations across these gradients. Gradient analysis of landscape ecological risks typically concentrates on areas with significant topographical fluctuations, such as the Loess Hilly–Gully Region, watershed areas, and ecologically fragile regions; however, studies on the gradient of ecological risks in coastal areas are relatively scarce.

The coastal zone, as an interwoven zone of land and sea systems, is an independent environmental system with two distinct land and ocean features [20]. The coastal zone is a typical ecologically vulnerable zone and a sensitive area to environmental changes since it has one of the highest densities of development and utilization [21]. On the one hand, coastal areas have to provide land and other resources for economic and social development, and on the other hand, they have to absorb the ecological and environmental pressures brought about by human activities and climate change. Consequently, while achieving rapid economic and social growth, these regions also confront numerous challenges, including unreasonable land use, coastal erosion, and regional development imbalances. In recent years, the dual pressures of global climate change and human activities have significantly altered coastal landscape patterns, leading to noticeable changes in landscape ecological processes and subjecting coastal zones to immense environmental pressures and ecological risks. Most of the research on coastal landscape and ecological risk is focused on large-scale global and national coastal climate and environmental changes [22,23], the ecological environment quality of the coastal zone [24,25,26,27], the ecosystem service value [28,29,30], as well as land use in cities and urban agglomerations of a mesoscale system [31,32,33,34]. There is a lack of research on the landscape pattern and ecological risks of coastal cities taking into account coastal features.

To specifically study the spatial and temporal changes in landscape patterns and ecological risks in coastal city, this study introduces the concept of the sea–land gradient. Gradient analysis was once a commonly used method in ecology, with similar migrations occurring in urban–rural gradients [35,36,37], topographical gradients, hydrothermal gradients, and elevation gradients. The sea–land gradient provides a new perspective to quantify the characteristics of the spatial distribution of coastal zones. It is an independent environmental system with sea–land transition features that can express the spatial change pattern of landscape structure from sea to land in a certain range [38]. By thoroughly analyzing the landscape pattern characteristics between the sea–land gradient zones can help to understand more comprehensively the landscape structure, function, and dynamic change process along this gradient. Additionally, conducting landscape ecological risk assessments based on the sea–land gradient allows for a more accurate exploration of the spatial patterns of ecological risk changes between land and sea. The changing landscape pattern and ecological development of coastal cities are both influenced by the sea–land gradient. An integrated analysis of the landscape pattern index, the landscape ecological risk index (ERI), and the natural and human elements of the sea–land gradient can better explain the interdependence of landscape processes and natural and socioeconomic processes [39].

The ecological and environmental issues in the coastal zone have garnered significant attention both domestically and internationally, according to the studies that are currently available. However, compared to watershed and inland areas, the theories and techniques for evaluating ecological risks in coastal regions remain relatively underdeveloped and require further in-depth and detailed investigation. Few studies have specifically analyzed the sea–land gradient effects on landscape patterns and ecological risks in coastal areas. To solve these challenges and more precisely explore the gradient effects of landscape patterns and ecological risks in coastal regions, this study introduces the concept of the sea–land gradient, constructs gradient intervals, and proposes landscape pattern indices and ecological risk indices applicable to the sea–land gradient. Using Dalian as a study area, this study analyzes the spatiotemporal changes in landscape patterns and landscape ecological risks along the sea–land gradient from 2000 to 2015. The study helps to deepen our understanding of the relationship between human activities and the sea–land interface, aiming to provide scientific support for the sustainable development of the coastal landscape in Dalian. Additionally, it offers theoretical insights for regional land-use planning and urban development decision-making. This study holds significant importance for coordinating the rational use of land resources, regional sustainable development, and environmental protection in coastal zones.

2 Materials and methods

2.1 Study area

Dalian is located at the southern end of the Liaodong Peninsula, between 120°58′–123°31′E and 38°43′–40°10′N. It is bounded to the east by the Yellow Sea, to the west by the Bohai Sea, to the south by the Shandong Peninsula, and to the north by China’s northeastern mainland. Dalian is surrounded by the sea on three sides and a long coastline, and blessed with the advantages of marine resources. The topography of Dalian City is characterized by higher elevations in the north and lower elevations in the south, with mountainous and hilly landscapes widely distributed. Dalian City is located in the warm temperate zone of the Northern Hemisphere and experiences a warm temperate continental monsoon climate with maritime characteristics. The winters are mild, the summers are not excessively hot, and the four seasons are distinct. The annual average temperature is 10.5°C, with annual precipitation ranging from 550 to 950 mm, and the total annual sunshine hours range from 2,500 to 2,800 h. The total area of the city is approximately 12,574 km2, of which the total area of the urban area is approximately 550.27 km2. For this study, the land area of Dalian is divided into the Main urban area, Jinzhou District, Lushunkou District, Pulandian District, Wafangdian City, and Zhuanghe City (Figure 1).

Figure 1 Location and administrative divisions of Dalian.
Figure 1

Location and administrative divisions of Dalian.

2.2 Data sources

This study is based on the land-use data from the Resource and Environment Data Center of the Chinese Academy of Sciences from 2000 to 2015 [40]. Based on the national resource and environment database, the Chinese Academy of Sciences utilized Landsat remote sensing imagery from the United States as the primary information source. Through manual visual interpretation, they established a national-scale land-use thematic database at a 1:10,000 scale, covering multiple time periods. The land-use data required for this study for the years 2000, 2005, 2010, and 2015 was obtained through reclassification processing, with a spatial resolution of 30 meters (Figure 2). The classification of this study is based on the national standard of Classification of Land Use Status (GB/T21010-2017), and combined with the characteristics of the land-use types in the study area, the land-use types of Dalian City are divided into agricultural land, woodland, grassland, water bodies, construction land, and unused land. The construction land includes urban building sites, rural building land, and other building sites (Table 1).

Figure 2 Spatial distribution of land use in Dalian.
Figure 2

Spatial distribution of land use in Dalian.

Table 1

Land-use classification categories

Land-use class Secondary land-use category
Agricultural land Paddy field
Dryland
Woodland Arbor
Shrubland
Sparse woodland
Other forest lands
Grassland Natural pasture
Artificial pasture
Other grasslands
Water bodies River
Lake
Reservoir
Pit pond
Channel
Construction land Urban building sites
Rural building land
Other building sites
Unused land Saline-alkali soil
Sand
Bare land

According to our previous researches, during 2000 and 2015, Dalian has fully utilized the benefits of coastal resources, and the entire quantity of land for the urban and port building has been spread near the coastline area, with a quick growth rate. The main source of land for building in the coastal zone is the conversion of agricultural land by human activity. The major zone of urban construction land has expanded from the shore to the inland in the southern part of Dalian; in the center and northern portions, urban construction land has developed inland, on the one hand, and to the sea on the other. From a spatial perspective, the homogeneity of land use gradually decreases from the coast to the interior; from a temporal perspective, the homogeneity of administrative units gradually increases, except for the Dalian city area. In the course of development, the homogeneity of land use in Dalian increases with time and subsequently diminishes when it reaches a particular level. Overall, the coastal areas are the first to suffer [41].

2.3 Methods

2.3.1 Extracting coastline and sea–land gradient

Since 2000, the coastline of Dalian has been changing as a result of the interaction between sea and land. The urban expansion has moved toward the sea, and part of the sea areas has transformed into land continually. In this study, the coastline extracted in 2015 is used as the reference coastline, which is the boundary of the study area.

To further analyze the spatial and temporal changes in coastal landscape patterns and landscape ecological risks with the spatial distance of the coastline, this study introduces the concept of sea–land gradient. According to our previous research [41], the sea–land gradient can reflect the spatial variation of landscape structure within a certain range from the sea to the land. It is an independent environmental system with the characteristics of sea–land transition. Based on the reference coastline, the study area is divided into four zones of [0,2.5] km, [2.5,5] km, [5,10] km, and [10, Max] km from sea to land, respectively (Figure 3).

Figure 3 Distribution of sea–land gradient and coastline changes in Dalian.
Figure 3

Distribution of sea–land gradient and coastline changes in Dalian.

2.3.2 Measuring landscape pattern index with sea–land gradient

The landscape pattern index is widely used to analyze the spatial heterogeneity of urban ecosystems and their ecological effects [42]. In this study, two types of landscape indices are studied: class metrics and landscape metrics. The number of patches (NP), patch density (PD), edge density (ED), aggregation index (AI), and percentage of landscape (PLAND) are examples of class metrics indices; landscape metrics indices include the landscape shape index (LSI), largest patch index (LPI), Shannon’s evenness index (SHEI), and contagion index (CONTAG) [43,44,45] (Table 2). First, we cut out the land-use raster data of different buffer zones in batches and then calculated landscape pattern metrics in batches in Fragstats 4.2 software. Fragstats is a software tool used for analyzing and describing landscape patterns. It was developed by the Forest Service of the United States Department of Agriculture (USDA) and initially made available in 1994. The software is designed to provide quantitative analytical methods for landscape ecology research, enabling the assessment and interpretation of spatial and temporal patterns and dynamics within landscapes.

Table 2

Landscape pattern index

Landscape index Metrics Formula and meaning
NP Class/landscape NP = N . Describe the NP in each landscape type.
PD Class/landscape PD = N A × 10 , 000 × 100 . The NP per unit area in a landscape includes all heterogeneous patches of landscape elements, reflecting the degree of fragmentation of the landscape.
ED Class ED = ∑ k = 1 m e ik A × 10 , 000 . Reflects the dynamic character of the landscape elements.
AI Class AI = g ii max → g ii . Reflects the degree of aggregation in the landscape, with larger values indicating that the landscape consists of a few large clustered patches.
PLAND Class PLAND = ∑ i = 1 n a ij A × 100 . PLAND equals the percentage of the landscape comprising the corresponding patch type.
LPI Landscape LPI = Max ( a 1 , a 2 , … a n ) A × 100 % . LPI equals the percentage of the landscape comprised by the largest patch.
LSI Class/landscape LSI = 0.25 E A . The larger the LSI, the more complex the shape of the plaque.
SHEI Landscape SHEI = − ∑ i = 1 n P i × ln ( P i ) ln ( n ) . Describes the degree of uniformity in the distribution of the components in the landscape, the more the SHEI tends to 1, the more uniform the distribution of the landscape.
CONTAG Landscape CONTAG = 1 + ∑ i = 1 m ∑ j = 1 n P ln P ij 2 ln ( m ) . Reflects the tendency for the different patch types in the landscape to spread out or cluster together.

2.3.3 Measuring landscape ERI with sea–land gradient

To improve the accuracy of estimating and visualizing the ERI, the landscape ecological units need to first be divided. According to landscape ecology studies, units should be 2–5 times larger than average patches. For the irregular units cutoff at the edge of the study area, those with an area greater than 0.5 regular sampling units are considered separate sample units, while those with an area less than 0.5 regular sampling units are merged into nearby sampling units [46]. As a result, the study area was divided into 3 km × 3 km ecological risk assessment units and divided into 1,356 units in total. The ERI was calculated in each assessment unit, and the results were assigned to the central pixel of the evaluation area.

Landscape pattern indices can effectively quantify valuable information regarding land-use/cover patterns and have thus become important indicators for assessing regional ecological risk conditions [47]. The magnitude of these indices can reflect the level of landscape ecological risk in the study area. The equation for landscape ecological risk is defined as follows:

(1) ERI i = ∑ i = 1 N A ki A k × R i

where ERI i is the landscape ERI for the ith risk cell, A ki represents the area of the ith landscape type in the kth risk cell, A k is the total landscape area of the kth risk cell, i denotes the number of landscape types within the risk unit, and R i is the landscape loss index. The various indices in the model are calculated as follows:

2.3.3.1 Landscape loss index

The landscape loss index ( R i ) is used to measure the degree of change in the internal structural characteristics of different landscape types due to natural or human disturbances, as well as the degree of loss of natural attributes. It is commonly represented by combining the landscape disturbance index and the landscape vulnerability index [47]. The formula is as follows:

(2) R i = S i × F i

where S i is the landscape disturbance index, and F i is the landscape vulnerability index.

2.3.3.2 Landscape disturbance index

The landscape disturbance index ( S i ) is composed of three components: the landscape fragmentation index ( C i ), the landscape separation index ( N i ), and the landscape fractal dimension index ( E i ). These indices together effectively represent the extent to which ecosystems, represented by different landscape types, are impacted by human activities [48]. The formula is as follows:

(3) S i = a C i + b N i + c E i

where C i , N i , a n d E i represent the landscape fragmentation index, landscape separation index, and the landscape fractal dimension index, respectively. The parameters a, b, and c denote the weights assigned to each index, with the condition that a + b + c = 1. Based on existing studies on coastal landscape ecological risk and balanced analysis [49], it is determined that the landscape fragmentation index carries the highest weight, assigned a value of 0.5, followed by the landscape separation and fractal dimension indices, with values of 0.3 and 0.2, respectively.

2.3.3.2.1 Landscape fragmentation index

The landscape fragmentation index ( C i ) refers to the process by which a landscape, originally uniform, homogeneous, and continuous, transitions into a complex, heterogeneous, and discontinuous mosaic of patches under the influence of natural changes or human activities [50]. The formula is as follows:

(4) C i = n i A i

where n i represents the NP of the ith landscape type, and A i represents the total area of the ith landscape type.

2.3.3.2.2 Landscape separation index

The landscape separation index ( N i ) is used to quantify the degree of separation between individual patches of a specific landscape type [51]. A higher value of this index indicates a more dispersed distribution of the landscape. The formula is as follows:

(5) N i = A 2 A i n i A i

where n i represents the NP of the ith landscape type, A i denotes the area of the ith landscape type, and A refers to the total area of all landscape types.

2.3.3.2.3 Landscape fractal dimension index

The landscape fractal dimension index ( E i ) measures the complexity of patch shapes in a landscape after being subjected to external disturbances. This index primarily reflects the extent of human impact on the natural environment. A higher value indicates more complex patch shapes [52]. The formula is as follows:

(6) E i = 2 ln ( P i 4 ) / ln A i

where A i is the area of the ith landscape type, and P i is the perimeter of the ith landscape type.

2.3.3.3 Landscape vulnerability index

The landscape vulnerability index ( F i ) reflects the sensitivity and fragility of different landscapes to external disturbances [53]. Higher vulnerability indicates weaker resistance of the landscape type to disturbances. Since different landscape types have varying ecological functions and different abilities to resist external disturbances, unused land is the most vulnerable, while construction land is relatively stable. Drawing on the existing literature [52,54], and taking into account the characteristics of the study area, the vulnerability values for various landscape types were assigned and normalized. The vulnerability indices for eight land-use types are as follows: unused land 0.26, water bodies 0.21, agricultural land 0.17, grassland 0.13, woodland 0.09, rural building land 0.06, urban building sites 0.04, and other building sites 0.04.3.

3 Results

3.1 Spatial and temporal changes of coastal landscape patterns

3.1.1 Gradient analysis of coastal landscape pattern

The results of the calculation of the landscape pattern index for different gradient zones in Dalian from 2000 to 2015 are shown in Table 3. The main observations may be summarized as follows:

  1. From 2000 to 2015, NP and LSI showed a tendency of first dropping and then growing along with the distance buffer zones in Dalian. NP has the fastest growth rate and the largest number in the [10, Max] km zone. The landscape shape is relatively irregular in the [0,2.5] and [10, Max] km zones.

  2. PD decreases as the distance increases, indicating that the degree of landscape fragmentation decreases as the distance from the coastline, and the highest degree of landscape fragmentation is found in the [0,2.5] km zone.

  3. In contrast to the changing trend of PD, the LPI grows with distance when seen from a spatial perspective. The LPI declined throughout time, with the rate of decline in the [10, Max] km zone being the fastest, at 64%.

  4. The CONTAG increases steadily as the distance from the coastline increases, and the value is greatest in the [5, Max] km zone, indicating a significant degree of landscape aggregation. The degree of aggregation declines year after year.

  5. The SHEI has increased year by year. The growth rate is the fastest in the [0,2.5] km zone, and the distribution of components in the landscape is the highest.

Table 3

Landscape pattern index (landscape metrics) for different buffer zones in Dalian, 2000–2015

Year Buffer zones NP PD LPI LSI CONTAG SHEI
2000 [0,2.5] 3646.00 1.56 4.97 46.29 62.13 0.66
[2.5,5] 1956.00 1.33 5.73 36.40 66.71 0.57
[5,10] 2465.00 1.19 17.54 37.63 70.93 0.49
[10, Max] 4445.00 0.71 29.45 56.90 69.13 0.52
2005 [0,2.5] 4055.00 1.72 4.94 45.70 60.63 0.69
[2.5,5] 2001.00 1.36 5.72 34.77 65.96 0.59
[5,10] 2523.00 1.22 14.04 36.42 70.55 0.50
[10, Max] 4972.00 0.80 23.99 54.57 68.95 0.53
2010 [0,2.5] 5016.00 2.09 4.45 49.79 56.72 0.76
[2.5,5] 2212.00 1.50 4.97 38.83 62.48 0.64
[5,10] 2873.00 1.39 9.57 43.20 67.46 0.54
[10, Max] 6142.00 0.99 10.75 66.85 67.15 0.55
2015 [0,2.5] 3433.00 1.41 4.87 47.57 56.44 0.77
[2.5,5] 2155.00 1.46 4.88 38.26 62.06 0.65
[5,10] 2886.00 1.39 9.55 43.50 66.68 0.55
[10, Max] 6112.00 0.98 10.74 66.84 66.63 0.55

Figure 4 reports the changes in the landscape index at the class metrics on different buffer zones from 2000 to 2015. The degree of fragmentation and aggregation in different buffer zones, and the spatial distribution of landscape types revealed zonal differences.

  1. From 2000 to 2015, the varying pattern among the different buffer zones was the same for rural building land, woodland, and agricultural land. The specific performance is that the LSI, PD, ED, and the NP all have maximum values, the landscape seems fragmented, and the landscape shape is irregular.

  2. The patch shape of rural building land becomes increasingly irregular as the distance from the coastline rises. The NP of rural building land in the [10, Max] km zone has grown at the quickest rate, increasing by 1,583 patches in 15 years.

  3. The urban building sites have the highest landscape AI value. In comparison with other land types, urban building sites are the most concentrated, with coastal development focused on the [0,5] km zone.

  4. Woodland and agricultural land have the biggest PLAND types. The number of woodland increases with the distance from the coastline, and the proportion is largest in the [10, Max] km zone. The proportion of agricultural land is largest in the [5,10] km zone. In 2015, the proportion of urban building sites in the [0,5] km zone more than doubled compared to 2000.

Figure 4 Changes in landscape pattern index (class metrics) for different buffer zones in Dalian from 2000 to 2015: (a) NP, (b) PD, (c) ED, (d) AI, (e) LSI, and (f) PLAND.
Figure 4

Changes in landscape pattern index (class metrics) for different buffer zones in Dalian from 2000 to 2015: (a) NP, (b) PD, (c) ED, (d) AI, (e) LSI, and (f) PLAND.

3.1.2 Spatial analysis of gradient effect of coastal landscape pattern

The analysis of the landscape pattern indices of each administrative district in 2000 and 2015 (Table 4) could be summarized in the following points:

  1. From 2000 to 2015, the landscape of the main urban area gradually fragmented from land to sea. Particularly, in the [0,2.5] km zone, the fragmentation is significant, the shape of the plaque is relatively regular, and the distribution of the plaque is highly uniform.

  2. The Jinzhou District is highly fragmented overall, with patches clustering as the distance from the coastline rises and the distribution of the landscape becomes less uniform, with the largest fragmentation in the [10, Max] km zone.

  3. The degree of landscape fragmentation in the Lushunkou District is large in the [0,5] km zone. However, as the distance between buffer zones increases, the degree of fragmentation gradually reduces, the shape of the patches tends to be regular, and the aggregation of landscape components is high.

  4. The Pulandian District has the highest degree of landscape fragmentation and has the maximum density of patches when compared to other administrative regions in the [0,2.5] km zone. The degree of fragmentation in the Pulandian District steadily reduces as the distance from the coastline increases, and the landscape shape is irregular.

  5. In Wafangdian City, the buffer zone near the sea has the most landscape fragmentation, while the buffer zone near the land has more patchy aggregation.

  6. The NP in Zhuanghe City has increased fast over time, particularly in the [10, Max] km zone, where there has been a rise of 885 patches. Simultaneously, the landscape shape is most regular in the [10, Max] km zone. The [0,10] km zone has a higher degree of landscape fragmentation and patch aggregation.

Table 4

Landscape pattern index (landscape metrics) by administrative districts in Dalian

Buffer zones Administrative district NP PD LPI LSI CONTAG SHEI
2000 2015 2000 2015 2000 2015 2000 2015 2000 2015 2000 2015
[0,2.5] Main urban area 297.00 232.00 1.04 0.76 26.03 39.13 15.31 15.26 58.26 60.42 0.75 0.71
Jinzhou District 788.00 681.00 1.45 1.16 9.40 9.21 23.45 22.68 59.75 54.10 0.71 0.82
Lushunkou District 338.00 365.00 1.35 1.44 17.22 9.19 16.14 17.84 63.47 57.67 0.62 0.73
Pulandian District 489.00 339.00 2.71 1.88 48.33 48.78 14.70 13.93 63.45 63.70 0.62 0.63
Wafangdian City 1000.00 817.00 1.59 1.23 9.68 8.58 25.44 24.75 66.19 59.33 0.57 0.72
Zhuanghe City 757.00 716.00 1.71 1.61 26.17 17.26 17.67 22.02 73.75 67.38 0.45 0.55
[2.5,5] Main urban area 190.00 144.00 1.23 0.94 14.29 40.96 12.50 10.57 60.00 66.73 0.69 0.58
Jinzhou District 528.00 530.00 1.44 1.44 9.55 17.80 19.03 18.56 64.80 62.39 0.61 0.65
Lushunkou District 181.00 209.00 1.30 1.50 23.06 21.77 11.29 12.38 69.18 61.17 0.52 0.66
Pulandian District 302.00 319.00 1.68 1.78 43.09 26.80 13.01 14.04 63.88 67.79 0.60 0.54
Wafangdian City 506.00 521.00 1.45 1.49 13.20 9.52 19.00 19.64 67.80 58.28 0.54 0.72
Zhuanghe City 313.00 505.00 1.11 1.79 29.84 25.46 14.39 18.27 70.74 70.16 0.49 0.49
[5,10] Main urban area 77.00 74.00 0.57 0.54 70.26 31.28 6.85 8.41 70.63 67.55 0.51 0.56
Jinzhou District 661.00 683.00 1.40 1.44 30.12 15.66 20.75 22.52 70.06 64.87 0.49 0.58
Lushunkou District 74.00 104.00 0.83 1.17 39.96 29.51 7.27 8.82 63.01 66.25 0.63 0.57
Pulandian District 570.00 616.00 1.45 1.56 56.01 22.32 15.98 18.43 76.56 72.89 0.38 0.44
Wafangdian City 683.00 723.00 1.36 1.44 32.71 39.55 21.33 22.76 67.33 64.85 0.53 0.57
Zhuanghe City 501.00 810.00 1.05 1.70 33.14 18.35 17.41 22.69 71.36 70.03 0.47 0.48
[10, Max] Main urban area 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Jinzhou District 362.00 346.00 1.58 1.51 70.66 71.32 13.91 13.88 68.46 71.19 0.50 0.46
Pulandian District 1573.00 1980.00 0.93 1.17 39.12 35.14 31.25 36.99 68.29 66.80 0.53 0.54
Wafangdian City 1255.00 1707.00 0.69 0.94 22.78 16.34 29.41 36.52 69.01 64.34 0.53 0.60
Zhuanghe City 1393.00 2278.00 0.56 0.92 19.77 19.82 36.90 41.78 61.12 66.27 0.65 0.56

To summarize, since the twenty-first century, the landscape pattern of land use in Dalian has been changing. The changes are mainly manifested in the gradual fragmentation of the landscape, the increasing NP, the complex shape of patches, and the irregularity of patch boundaries. It appears that as the distance from the coastline rises, the degree of fragmentation decreases, and fragmentation of the landscape within the [0,2.5] km zone is large, although the uniformity of the landscape distribution is quite high. In terms of land-use types, urban building sites develop along the coastline and grow in the [0,5] km zone, whereas rural building land expands rapidly in the [10, Max] km zone. Agricultural land and woodland are the dominant landscape types in Dalian, accounting for the greatest proportion, and the greatest proportion is located in the [5, Max] km zone (the proportion is greater than 40%). The proportion of landscape types of urban building sites increased from 6.08% in 2000 to 15.23% in 2015 within the [0,2.5] km zone, which indicates significant urbanization development. Rural building land, woodland, and agricultural land are all highly fragmented. In terms of administrative regions, Jinzhou District is significantly fragmented.

3.2 Spatial and temporal changes in landscape ecological risk

3.2.1 Gradient analysis of coastal landscape ecological risk

The ecological risk grade for each buffer zone in Dalian from 2000 to 2015 was calculated according to the landscape ERI model (equation (1)) and then assigned to each unit centroid, and the Kriging method was used to generate a map of the spatial distribution of ecological risk in Dalian (Figure 5). Based on the natural breaks classification, the ecological risk was divided into five risk levels: low, medium low, medium, medium high, and high.

Figure 5 Spatial distribution of ecological risks in Dalian from 2000 to 2015.
Figure 5

Spatial distribution of ecological risks in Dalian from 2000 to 2015.

Table 5 shows how the area of ecological risk in the various buffer zones changed between 2000 and 2015. In Dalian, low risk grew over time, medium high and high shrank, and the medium risk had a disproportionately high percentage. The low risk is expanding in the [2.5,5] km zone, and this buffer zone is primarily dominated by the medium risk. Low risk in the [5,10] km zone fluctuates up and down with time, and this zone is dominated by medium and medium high risk, the area of which increases with time. In the [10, Max] km zone, low and medium low risk decreased over time, while the area of medium risk increased until 2015, and the area of high and medium high risk decreased, with medium and medium high zones dominating in the [10, Max] km zone.

Table 5

Changes in coastal ecological risk gradients in Dalian

Buffer zones Year Low (km2) Medium low (km2) Medium (km2) Medium high (km2) High (km2)
[0,2.5] 2000 197.31 447.46 765.48 510.80 415.69
2005 227.90 387.46 717.16 553.88 450.34
2010 313.05 578.32 476.31 550.87 418.19
2015 517.68 464.11 787.21 330.27 237.47
[2.5,5] 2000 103.63 420.08 599.38 297.89 52.15
2005 136.38 374.07 614.64 300.67 47.38
2010 140.97 449.77 421.34 377.45 83.61
2015 196.85 286.51 608.85 337.25 43.67
[5,10] 2000 78.53 511.56 877.38 459.07 142.68
2005 107.35 459.23 884.81 487.37 130.47
2010 50.60 578.91 587.99 682.24 169.48
2015 65.45 291.43 901.28 669.69 141.38
[10, Max] 2000 441.87 1350.72 2571.94 1470.05 371.15
2005 399.52 1228.57 2615.13 1541.92 420.58
2010 276.94 1524.39 1616.28 2193.07 595.05
2015 274.88 963.50 2316.94 2188.55 461.85

The transfer matrix of ecological risk-level changes in distance from the coastline in Dalian is presented in Figure 6. The results show that:

  1. In the [0,2.5] km zone, the transfer from medium low to medium risk is the key factor in the 204.66 km2 region with an increasing trend of ecological risk level; the transfer from medium to medium low risk is the main factor in the 475.65 km2 area with a declining trend of the ecological risk level. The decreasing trend in this buffer zone is 2.3 times greater than the increasing area, indicating a gradual decrease in the ecological risk level in the buffer zone near the sea.

  2. In the [2.5,5] km zone, the area with an increasing trend in ecological risk level is 341.83 km2, consisting primarily of medium low to medium risk and from medium to medium high risk; the area with a decreasing trend in ecological risk level is 160.66 km2, consisting primarily of medium to low and medium low risk.

  3. In the [5,10] km zone, the area with an increasing trend of ecological risk level is 666.02 km2, mainly from medium low to medium risk and from medium to medium high risk; the area with a decreasing trend of ecological risk level is 95.69 km2, mainly from medium to medium low risk. In this buffer zone, the area with an upward trend is 7 times larger than the area with a downward trend, and the ecological risk level rises significantly.

  4. In the [10, Max] km zone, the area with an increasing trend in ecological risk level is 1894.27 km2, primarily because of the transfer of medium low risk to medium and medium high risk, as well as the transfer of medium risk to medium high risk; the area with a decreasing trend in ecological risk level is 358.13 km2, primarily because of the transfer of medium risk to medium low risk. This buffer zone’s upward trend is 5.3 times greater than its downward trend, indicating a significant increase in the level of ecological risk.

Figure 6 Transfer matrix of ecological risk level changes in (a) [0,2.5] km, (b) [2.5,5] km, (c) [5,10] km, and (d) [10, Max] km from the coastline in Dalian.
Figure 6

Transfer matrix of ecological risk level changes in (a) [0,2.5] km, (b) [2.5,5] km, (c) [5,10] km, and (d) [10, Max] km from the coastline in Dalian.

Figure 7 Ecological risk in the administrative district of Dalian from 2000 to 2015: (a) main urban area, (b) Jinzhou District, (c) Lushunkou District, (d) Pulandian District, (e) Wafangdian City, and (f) Zhuanghe City.
Figure 7

Ecological risk in the administrative district of Dalian from 2000 to 2015: (a) main urban area, (b) Jinzhou District, (c) Lushunkou District, (d) Pulandian District, (e) Wafangdian City, and (f) Zhuanghe City.

In the study area, the ecological risk grade is generally transferring from medium low to medium high levels. The area with a decreasing trend in ecological risk is 1090.13 km2, whereas the area with an increasing trend is 3106.78 km2, or almost 2.85 times the area with a declining trend. Except for the buffer zone near the sea, the ecological risk levels in the rest of the gradient are all increasing more than decreasing, and the closer to land, the greater the increase than the decrease, indicating that the overall ecological risk in the coastal zone of Dalian is on the high side during the period 2000–2015.

3.2.2 Spatial analysis of gradient effect of coastal landscape ecological risk

Administrative units in Dalian displayed varying levels of ecological risk from 2000 to 2015 in various distance buffer zones (Figure 7). Except for the main urban area and Lushunkou District, each administrative unit’s trend of change in the transfer of ecological risk areas occurred within the same buffer zone between 2000 and 2015; however, the rate and area of transformation varied.

In the [0,5] km zone, there is mainly a transfer from high risk to low risk. However, in the [5, Max] km zone, there is a complex transfer in ecological risk levels, both from low to high and from high to low. The main urban area and Lushunkou District have a unique trend of change. The main urban area and Lushunkou District are similar in size and narrow in shape, and are adjacent to each other, with the Yellow Sea and the Bohai Sea on the east and west sides. There is no [10, Max] km zone in the main urban area and Lushunkou District due to the influence of area and shape factors, and the transfer in ecological risk is primarily manifested in the [0,2.5] km zone, from high risk to low risk. In comparison with the main urban area, Lushunkou District, and Jinzhou District, Pulandian District, Wafangdian City, and Zhuanghe City are located inland in the north. Pulandian District, Wafangdian City, and Zhuanghe City have the same trend of change in the [10, Max] km zone, with the largest area of change.

To summarize, the ecological risk level in Dalian’s near-sea zones has been declining annually between 2000 and 2015, while the opposite has been true for the near-land zones. The majority of the ecological risk-level transfer from Dalian is from low level to high level. In the [0,2.5] km zone, the ecological risk level is trending downward; however, in the [2.5, Max] km zone, it is trending upward. The overall ecological risk level of Dalian City is relatively high, and from a spatial perspective, the ecological risk level displays a gradient-like increasing tendency as the distance from the coastline rises.

4 Discussion

According to the results presented earlier, the coastal development patterns of Dalian have changed dramatically from 2000 to 2015 and have a significant impact on coastal land-use patterns.

First of all, there is a significant sea–land gradient effect on the changes of landscape patterns and landscape ecological risks in Dalian. The landscape patterns near the seaside have a tendency of more fragmentation than near the landside. The landscape ecological risks in the coastal zone have been increasing relative to those in the other zone. These results indicate that the development of Dalian is along the coastal zone and land use has been diversified, fragmented, and sensitive with the development.

In addition, the spatial heterogeneity of the sea–land gradient effects is certified in this case study. Different regions have different landscape ecological risk levels due to their geographical locations and development orientations. For example, the ecological risk values of Jinzhou District and Pulandian District have increased, with medium and medium high ecological risks predominating. In contrast, the main urban area is relatively well developed, with a high proportion of construction land and relatively sound facilities, and the ecological risk of the landscape is low. This is a result of the quick expansion of Jinzhou District, which led to the state’s approval of the establishment of Jinpu New District in 2014, which encompasses all of Jinzhou District’s administrative territory as well as a portion of Pulandian City. Throughout the study period, there was greater contact between construction land, unused land, and coastal water bodies, and the rapid development also generated ecological risk issues.

Changes in land use will unavoidably result in changes in regional landscape patterns, which will result in changes in landscape ecological risks [55]. The aforementioned phenomenon is the result of two major factors: land-use policies and the impact of rapid urbanization. Since the promulgation of the People’s Republic of China’s Land Management Law in 1999, land policy has begun to shift toward rationalization and standardization. From 2000 to 2015, the Ministry of Natural Resources issued 23 land-use policies and regulations and 103 marine development policies and regulations. Land policies regulate and influence the scale, intensity, and economic efficiency of land use [52]. At the same time, coastal cities have also emerged as the “leaders” of China’s economic development as a result of the “Northeast Revitalization Plan” and the brisk maritime business development. Furthermore, Dalian has an excellent geographical location and is the largest port city in northeast China. As the first batch of coastal open cities in China, Dalian has a relatively high level of urbanization and a rapid urbanization process. The change in landscape pattern is not only a concrete manifestation of the outcome of urbanization, but also a reflection of the dynamic change process of society, economy, environment, and many other factors affected by urbanization [56].

To a certain extent, the alterations in socioeconomic development and policy considerations over the past few years can be seen in the characteristics of land-use landscape patterns across time as well as the response to the distance from the coastline. Landscape ecological risk assessment is a comprehensive tool for the best spatial allocation of land resources and the design of ecological resource management decisions. Measuring the degree of landscape ecological risk in a region from the standpoint of ecological process and spatial coupling in landscape ecology offers a novel approach to quantifying the ecological effects of land-use change [57,58].

Based on the analysis in this study, the following recommendations are proposed: to address issues such as landscape fragmentation and increased ecological risks caused by unsustainable land development during socioeconomic development, Dalian should adhere to a protection-first principle in its future development. In the specific management process, it is crucial to first focus on overall development, followed by a detailed analysis of the variations in regional landscape patterns and ecological risks across different buffer zones. Additionally, close cooperation among the administrative districts should be strengthened. Since the landscape structure and ecological risk levels vary significantly across different administrative districts, differentiated management strategies should be adopted based on varying levels of risk. In addition, management can be stratified within administrative districts according to the changes in landscape patterns and ecological risks across the sea–land gradient zones. In the buffer zone near the sea, it is important to balance the needs of economic development with the protection of landscape ecology. This can be achieved through optimizing urban expansion models, rationally planning land use, and controlling development intensity, ultimately ensuring a balance between economic growth and ecological protection. In the buffer zone near the land, sustainable development of industries such as agriculture and forestry should be promoted to enhance land-use efficiency and optimize landscape patterns. This will help to realize the rational and circular use of resources and improve the stability and resilience of ecosystems against disturbances.

By clarifying the spatiotemporal patterns of coastal landscape changes and ecological risks, this study helps inform better urban management and provides a reliable basis for future coastal city planning and sustainable ecological development. Given the complexity of landscape ecological elements in coastal zones, this study evaluates the ecological risks of Dalian’s coastal zone primarily from the perspective of land-use and landscape pattern changes, without fully accounting for the influence of natural and human factors. Future research should focus on potential risk sources involving natural and socioeconomic factors to clarify the role of natural and socioeconomic drivers in the changes in landscape patterns and ecological risks. We are currently collecting data on land-use changes of other coastal cities in the world and will conduct a more extensive analysis of the aforementioned conclusions in the future.

5 Conclusion

In the context of integrated land–sea planning and rapid urban development, exploring the impact of the land–sea gradient on the changes in landscape patterns and ecological risks of coastal cities is essential for better understanding coastal urban development. This understanding is crucial for addressing environmental and natural challenges. The research holds significant value for optimizing landscape patterns and reducing ecological risks in the coastal zone of Dalian. In this study, the land-use data of Dalian City from 2000 to 2015 were classified and processed. Then, the landscape pattern index was calculated, and the landscape ecological risk model was constructed. Landscape pattern analysis and landscape ecological risk evaluation of land-use changes in Dalian City along the sea–land gradient are performed using a 3 km × 3 km unit as the research scale. The findings reveal the characteristics of landscape pattern changes and the spatiotemporal evolution of ecological risks in Dalian’s coastal zone. This study aims to provide theoretical references for the rational development of land resources, the optimization of landscape patterns, and policy-making for the coastal zone in Dalian. The main conclusions of this study are as follows. Dalian’s landscape pattern as a whole showed a fragmentation trend from 2000 to 2015, and the landscape pattern showed different patterns of change with distance from the coastline. The landscape pattern close to the buffer zone near the sea is characterized by complex patch shapes, a high degree of landscape diversity and fragmentation, and drastic changes in landscape dynamics, according to landscape metrics. It implies that the closer an area is to the coast, the faster urbanization develops. In the [0,5] km zone, urban building sites have the highest degree of aggregation and relatively significant fragmentation; agricultural land and woodland are the inverses of urban building sites. Rural building land is the most fragmented in comparison with other land types. The ecological risk of the Dalian landscape gradually increased from 2000 to 2015, then moderated, primarily in the medium and medium high ecological risk. The ecological risk is medium high in Dalian’s three northern cities and relatively medium low in the main urban area due to the distribution of large areas of arable land and woodland in the northern part of the study area, which is more influenced by human activities. Ecological risks in the buffer zone near the sea are decreasing year by year, while in the buffer zone near the land are increasing annually. The transition between ecological risk grades is complex, with the transition from medium low to medium high levels dominating. The area of decreasing ecological risk in the buffer zone near the sea is larger than the area of increasing ecological risk, in contrast to the buffer zone near the land.

Acknowledgements

Thanks for the reviewers and editors for their constructive suggestions and comments.

  1. Funding information: This research has been supported by the National Natural Science Foundation of China, Grant Number 42101257, Department of Education of Liaoning Province, Grant Number LJKZ0972, Institute of Marine Sustainable Development of Liaoning Normal University.

  2. Author contributions: Conceptualization, J.Z., F.W., and Y.H.; formal analysis, J.Z. and D.W.; project administration and funding acquisition, J.Z.; investigation, J.Z., D.W., and Y.H.; writing – original draft and methodology, Y.H.; writing – review and editing, J.Z.; supervision, F.W. The authors applied the SDC approach for the sequence of authors. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: All authors declare that there is no conflict of interest.

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Received: 2023-11-13
Revised: 2024-10-08
Accepted: 2024-10-08
Published Online: 2024-11-12

© 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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  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
https://doi.org/10.1515/geo-2022-0722
Keywords for this article
coastal zone; sea–land gradient; landscape pattern; landscape ecological risk; Dalian City
Creative Commons
BY 4.0

Articles in the same Issue

  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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