Analysis of the Spatiotemporal Heterogeneity of Various Landscape Processes and Their Driving Factors Based on the OPGD Model for the Jiaozhou Bay Coast Zone, China

Wei Wang; Yecui Hu; Rong Song; Zelian Guo

doi:10.3390/land11010007

Wang, Wei;Hu, Yecui;Song, Rong;Guo, Zelian

2021-12-21 00:00:00

land Article Analysis of the Spatiotemporal Heterogeneity of Various Landscape Processes and Their Driving Factors Based on the OPGD Model for the Jiaozhou Bay Coast Zone, China 1 1 , 2 , 1 1 Wei Wang , Yecui Hu *, Rong Song and Zelian Guo School of Land Science and Technology, China University of Geosciences, 29 Xueyuan Rd, Beijing 100083, China; 3012200018@cugb.edu.cn (W.W.); 3012200013@cugb.edu.cn (R.S.); 3012210010@cugb.edu.cn (Z.G.) Key Lab of Land Consolidation and Rehabilitation, Ministry of Natural Resources of the People’s Republic of China, 37 Guanying Rd, Beijing 100035, China * Correspondence: huyc@cugb.edu.cn Abstract: To date, various studies have analyzed changes in the landscape but there are few studies which have explored landscape processes and the corresponding driving factors. This study makes up for this deﬁciency in the systematic theoretical exposition and the spatiotemporal analysis of landscape processes. The results show that the amount of arable land outﬂow and built-up land inﬂow have resulted in an increase of 92,311.11 ha of built-up land that is mostly distributed around the administrative center and along the coast of Jiaozhou Bay. The outﬂow of ecological land is a major resource for replenishing arable land, by 37,016.19 ha, especially in terms of the grassland that is distributed in the hilly areas west of Jiaozhou Bay. The outﬂow of the salt-ﬁeld, ﬁsh-farm and ecological land outﬂow have good connectivity, a large patch size, and an irregular shape. The ecological type, elevation, slope, and vegetation coverage are the four factors that have a great Citation: Wang, W.; Hu, Y.; Song, R.; inﬂuence on all landscape processes. A gentler slope and lower elevation, and proximity to cities Guo, Z. Analysis of the and towns land will produce more arable land outﬂow and built-up land inﬂow. However, arable Spatiotemporal Heterogeneity of land inﬂow and ecological land outﬂow are the opposite. This research will guide natural resource Various Landscape Processes and Their Driving Factors Based on the management for a rapidly developing coastal zone. OPGD Model for the Jiaozhou Bay Coast Zone, China. Land 2022, 11, 7. Keywords: landscape; spatiotemporal features; geographical detectors model; R “GD” package; https://doi.org/10.3390/ coastal zone land11010007 Academic Editor: Salvador García-Ayllón Veintimilla 1. Introduction Received: 29 November 2021 “Landscape” refers to an area, as perceived by people, the character of which results Accepted: 18 December 2021 from the action and interaction of natural and/or human factors [1]. The understanding Published: 21 December 2021 of the landscape emerges as a complex of perceived characteristic areas that are formed Publisher’s Note: MDPI stays neutral by natural factors and human activity. Current developments in remote sensing and geo- with regard to jurisdictional claims in information technologies facilitate the analysis of landscape patterns and their processes. published maps and institutional afﬁl- Landscape patterns and landscape change, at all scales, have been extensively analyzed [2]. iations. However, the processes of landscape change have rarely been the subject of current research. In this paper, we will take the discourse of “landscape processes” as the main outline to ex- pand the discussion of the article. “Pattern”, “scale”, and “process” are important concepts in landscape ecology. However, the theoretical explanation of the “process” is somewhat Copyright: © 2021 by the authors. insufﬁcient, with most studies paying close attention to the ecological processes [2,3]. Land- Licensee MDPI, Basel, Switzerland. scape pattern analysis should develop from the current description of the static pattern to a This article is an open access article dynamic pattern and link the pattern and process together [4]. Studies on the landscape distributed under the terms and change trajectory analysis (LCTA) have provided this chance. LCTA is an approach that is conditions of the Creative Commons used for calculating the relationship between present-day landscape patterns, their attached Attribution (CC BY) license (https:// values, and the past [5,6]. Landscape change trajectory forms a dynamic landscape type creativecommons.org/licenses/by/ 4.0/). Land 2022, 11, 7. https://doi.org/10.3390/land11010007 https://www.mdpi.com/journal/land Land 2022, 11, 7 2 of 23 that includes temporal and spatial features and reﬂects the landscape process in space. It connects different temporal scales and spatial patterns. It is a common research paradigm to analyze a landscape trajectory area and its landscape pattern index based on remote sensing image data [7–10]. Some studies have explored the trajectory of the change in the landscape index corresponding to different types of land cover [11]. A few scholars have used the Moran index and local indicator of spatial association (LISA) to analyze the spatial autocorrelation and heterogeneity of an ecological landscape loss trajectory [12]. Regression analysis is used for exploring the driving force mechanism. Based on regression analysis, some studies have explored the relationship between landscape trajectories and physical geographic features or socioeco- nomic factors [6,13,14]. At present, the methods used for exploring the forces that drive changes in the landscape are relatively simple, and mainly use empirical analysis methods or logistics regression analysis methods. Most research has been focused on the outcomes of landscape changes rather than the processes [5,6,15–19]. Spatial heterogeneity is a major feature of ecological and geographical phenomena. It refers to the uneven distribution of various geospatial attributes within a certain geo- graphical area, or, simply, spatial variation of attributes [20]. Spatial heterogeneity with local clusters is a popular approach that explores, spatially, local clustering regions with similarities in their geographical attributes. It has been addressed by hundreds of quan- titative measures in landscape geometry, LISA, Getis-Ord Gi [21–23], and geographically weighted regression (GWR). However, the geographical detector model can quantify the spatial stratiﬁed heterogeneity, which compares the spatial variance within a stratum and that between different strata [24,25]. The main idea of geographical detectors is that the study space is divided into subregions by variables, and the spatial variance within each subregion and among different subregions are compared to evaluate the determinant power of the potential explanatory variables. From 2010 to 2019, more than 100 research papers have been published using this model. The applications of the model are predominant in geographically local determinants or exploration of factors, spatial patterns, and in- vestigation of heterogeneity [26]. The general geographical detectors include four parts, where the core part is the factor detector that quantiﬁes the relative importance of different geographical variables. The other three parts are the interaction detector, risk detector, and ecological detector [26,27]. On 27 April 2021, the optimal parameters-based geographical detectors (OPGD) model was launched on the R “GD” package [28], which can choose the optimal partitioning method and quantity. This improves the operation magnitude of the geographical detector model and is not limited to the maximum number of lines, i.e., 32,769, in Excel. The bay area usually has better living conditions, industrial production conditions, and, thus, the gulf area is usually highly developed. The Jiaozhou Bay coastal zone is a typical area, and its landscape has changed greatly since the reform and opening up occurred in 1978. Therefore, this area is extremely rich in spatial and temporal characteristics of landscape processes and is of high research value. Furthermore, exploring landscape processes in the Jiaozhou Bay coastal zone helps us to understand the changing mechanism of the coastal landscape in the typical bay area of China and provides policy suggestions for land management and control in the highly developed coastal zone. The present study seeks to: (1) explain the connotation of the landscape processes and the possible drivers and driving mechanisms, (2) analyze the spatiotemporal heterogeneity of various landscape processes within the last 30 years in the coastal zone of the Jiaozhou Bay, and (3) explore the driving factors for the various landscape processes using the OPGD model. Finally, a few policy suggestions have been put forth for landscape process control in the Jiaozhou Bay coastal zone. Land 2022, 11, x 3 of 23 Land 2022, 11, 7 3 of 23 2. Materials and Methods 2. Materials and Methods 2.1. Theoretical Principle 2.1. Theoretical Principle Landscape processes are spatiotemporal changes in the landscape that can be per- Landscape processes are spatiotemporal changes in the landscape that can be per- ceived, and this perception can be visual or detected in remote sensing images. In maps ceived, and this perception can be visual or detected in remote sensing images. In maps and and remote sensing images, a landscape process is a process of transforming one land- remote sensing images, a landscape process is a process of transforming one landscape type scape type into another at different time and space scales and implies a change in the basic into another at different time and space scales and implies a change in the basic attributes attributes of a landscape (Figure 1). A landscape process in a region might contain many of a landscape (Figure 1). A landscape process in a region might contain many microcosmic microcosmic fragments in space and time. This process can also be measured using tools fragments in space and time. This process can also be measured using tools for the analysis for the analysis of spatiotemporal heterogeneity. However, such a dynamic landscape of spatiotemporal heterogeneity. However, such a dynamic landscape process pattern is process pattern is bound to be a pattern interwoven with different times and spaces. bound to be a pattern interwoven with different times and spaces. Figure 1. Landscape processes at different monitoring points in time. Figure 1. Landscape processes at different monitoring points in time. The driving factors for a landscape process include human beings exploiting the natu- The driving factors for a landscape process include human beings exploiting the nat- ral space, building construction on cultivated land, and other ways of moving the landscape ural space, building construction on cultivated land, and other ways of moving the land- types toward more artiﬁcial types. On the contrary, it might also include human beings scape types toward more artificial types. On the contrary, it might also include human creating natural habitats, natural succession and disaster damage, returning farmland to beings creating natural habitats, natural succession and disaster damage, returning farm- forest land, grassland, or wetland, and other ways for transforming landscape types to more land to forest land, grassland, or wetland, and other ways for transforming landscape natural landscape types. The visual expression of a landscape process in space presents a types to more natural landscape types. The visual expression of a landscape process in shift in the landscape type and the landscape trajectory. Administrative intervention and space presents a shift in the landscape type and the landscape trajectory. Administrative planning control are common landscape process management methods. intervention and planning control are common landscape process management methods. 2.2. The Study Area 2.2. The Study Area The research area used in this study is located on the south bank of the Shandong The research area used in this study is located on the south bank of the Shandong Peninsula, near the South Yellow Sea, and has a slightly fan-shaped distribution around the Peninsula, near the South Yellow Sea, and has a slightly fan-shaped distribution around semi-closed Jiaozhou Bay. The research area consists of eight districts, namely, Huangdao, the semi-closed Jiaozhou Bay. The research area consists of eight districts, namely, Huang- Jiaozhou, Chengyang, Jimo, Laoshan, Shibei, and Shinan District (Figure 2). The Dagu, dao, Jiaozhou, Chengyang, Jimo, Laoshan, Shibei, and Shinan District (Figure 2). The Xiaogu, Taoyuan, Moshui, Baisha, and Yanghe rivers ﬂow through this area, and large Dagu, Xiaogu, Taoyuan, Moshui, Baisha, and Yanghe rivers flow through this area, and amounts of sediment carried by the rivers form a tidal ﬂat in the northern part of Jiaozhou large amounts of sediment carried by the rivers form a tidal flat in the northern part of Bay. The vegetation type groups of forests in the study area are deciduous broad-leaved Jiaozhou Bay. The vegetation type groups of forests in the study area are deciduous broad- forest, including Quercus, Acer, Pistacia chinensis, etc. The highest point is Laoshan leaved forest, including Quercus, Acer, Pistacia chinensis, etc. The highest point is Mountain, with an elevation of 1091 m. Laoshan Mountain, with an elevation of 1091 m. The study area had a permanent resident population of 7,250,900 million as of the end of 2018, The study accounting area h for ad 77.18% a perm of anent the total resid population ent populat of ion of Qingdao, 7,250,9 and 00 mi produced llion as of t a gross he end of 2018, domestic product accoun (GDP) ting fo of r 77.18% o 104.0709fbillion the total population o Chinese yuan. Many f Qingnational dao, and pr development oduced a gross domesti zones are located c product within (this GDP) of 104 study area. .070 For 9 bi instance, llion Chinese the Huangdao yuan. Meconomic any nation development al develop- zone, the Hongdao high-tech zone, the west coast new development zone, and the Jiaozhou Land 2022, 11, x 4 of 23 Land 2022, 11, 7 4 of 23 ment zones are located within this study area. For instance, the Huangdao economic de- velopment zone, the Hongdao high-tech zone, the west coast new development zone, and the Jiaozhou airport economic zone are located here (Figure 2). The study area has a high airport economic zone are located here (Figure 2). The study area has a high development development intensity and is a typical coastal zone with a high opening intensity. intensity and is a typical coastal zone with a high opening intensity. Figure 2. Developing and highly developed region in the study area. Figure 2. Developing and highly developed region in the study area. 2.3. Data Source and Processing 2.3. Data Source and Processing The 30 m 30 m grid land considered the data of eight county-level districts in The 30 m × 30 m grid land considered the data of eight county-level districts in Qing- Qingdao from 1990, 2000, 2010, and 2018, which were obtained from the Natural Resources dao from 1990, 2000, 2010, and 2018, which were obtained from the Natural Resources and and Environmental Sciences Data of the Chinese Academy of Science [29]. The geographical Environmental Sciences Data of the Chinese Academy of Science [29]. The geographical data has data integrity, and the comprehensive accuracy is close to 90% [30]. data has data integrity, and the comprehensive accuracy is close to 90% [30]. In this study, the salt-ﬁeld and ﬁsh-farm landscape types were extracted by masking In this study, the salt-field and fish-farm landscape types were extracted by masking based on the land-use status maps and Google Earth satellite images for different years, based on the land-use status maps and Google Earth satellite images for different years, which were independently extracted as code 5 mentioned above. The marsh in the unused which were independently extracted as code 5 mentioned above. The marsh in the unused land was classiﬁed into wetland and waters, and the rest were merged according to the land was classified into wetland and waters, and the rest were merged according to the ﬁrst-level class. Then, the CNLUCC (dataset of land use and cover in China) classiﬁcation first-level class. Then, the CNLUCC (dataset of land use and cover in China) classification system was redivided into arable land, woodland, grassland, wetlands and waters, salt- system was redivided into arable land, woodland, grassland, wetlands and waters, salt- ﬁeld and ﬁsh-farm, built-up land, and unused land. The spatial database of the different field and fish-farm, built-up land, and unused land. The spatial database of the different landscape types in the study area was formatted, and their codes were set as 1, 2, 3, 4, 5, 6, landscape types in the study area was formatted, and their codes were set as 1, 2, 3, 4, 5, and 7, and are listed in Table 1. 6, and 7, and are listed in Table 1. Table 1. Reclassification of the landscape types based on the CNLUCC. The First-Level Type The Secondary-Level Type Reclassification Type 1 Arable land 11, 12 1 Arable land 21, 22, 23 2 Woodland 2 Woodland 24 1 Arable land 3 Grassland 31, 32, 33 3 Grassland 4 Water area 41, 42, 43, 44, 45, 46 4 Wetlands and Waters Land 2022, 11, 7 5 of 23 Table 1. Reclassiﬁcation of the landscape types based on the CNLUCC. The First-Level Type The Secondary-Level Type Reclassiﬁcation Type 1 11, 12 1 Arable land Arable land 21, 22, 23 2 Woodland 2 Woodland 24 1 Arable land 3 Grassland 31, 32, 33 3 Grassland 4 Water area 41, 42, 43, 44, 45, 46 4 Wetlands and Waters Urban and rural residential 5 51, 52, 53 6 Built-up land land/industrial land 61, 62, 63, 65, 66, 67 7 Unused land 6 Unused land 64 4 Wetlands and Waters 99 Ocean 99 4 Wetlands and Waters The driving factors selected in this study are as follows (Table 2). Most of the data in the following table have been clipped. For distance factors, the Generate Near Table in ArcGIS10.3 Tool was used. For the other factors, the sampling tool in ArcGIS10.3 was used. The relevant data were then linked in a table for the relevant spatial heterogeneity analysis. Table 2. Data source and format of driving force analysis. Number Data Description Data Source/Format The Data Unit Distance from the mean center of the X1 Google maps Meter development zone X2 Distance from rivers and lakes www.openstreetmap.org Meter Distance from shore (coastal X3 www.resdc.cn Meter administrative boundary) The 30 m 30 m grid of the X4 Meter Distance from cities and town land CNLUCC data The 30 m 30 m grid of ASTER X5 Slope Percent GDEM V3 The 30 m 30 m grid of ASTER X6 Elevation Meter GDEM V3 X7 NDVI in October 1998 www.resdc.cn No units of measure Ten thousand yuan per X8 GDP in 1995 www.resdc.cn square kilometer Population difference between 1995 The 1 km 1 km X9 People per square kilometer and 2015 grid/www.resdc.cn The 30 m 30 m grid of the 21, 22, 23, 31, 32, 33, 41, 42, 43, 45, 45, X10 Type of ecological land 46 on CNLUCC dataset CNLUCC dataset X11 Growth of ﬁxed investment www.shujuku.org Ten thousand yuan X12 The density of road network www.openstreetmap.org Kilometer per square kilometer 1 2 Normalized Difference Vegetation Index. Accessed on 18 December 2021. In this study, the landscape processes with a conversion area ratio of >1% were regarded as the main landscape processes, and those with an area ratio of <1% were ignored. In the analysis of the spatial autocorrelation and the driving force analysis, ﬁsh spots of 200 m 200 m were used as the basic unit for extracting the driving factor values and analyzing the spatial heterogeneity. The attribute values of the landscape processes belonging to this type were set to 1, whereas those not belonging to this type were set to 0. 2.4. The Research Methods 2.4.1. Spatiotemporal Characteristics for Analyzing the Landscape Processes (1) Dynamic attitude The dynamic attitude of a single landscape type can be used for representing the quantitative degree of change in the study area within a time range. The comprehensive dynamic attitude of the landscape types is integrated, and the transfer between the land- Land 2022, 11, 7 6 of 23 scape types is taken into full consideration to describe the overall intensity of change in the regional landscape type [31]. (2) Transfer matrix and landscape trajectory In this study, a transfer matrix has been used for analyzing the landscape processes in each decade from 1990 to 2018. The two maps corresponding to the two years were intersected and merged using ArcGIS10.3. The landscape trajectory values can be expressed by the formula: n1 n2 ni CT = 10 P + 10 P + + 10 P + + P (1) 1 2 i where CT represents the trajectory codes on each grid in the research time sequence, n represents the time node in the research time sequence number (n > 1), and P represents the raster data of the landscape type at the ith time node. 2.4.2. Exploring the Spatial Heterogeneity of Each Landscape Process (1) Landscape pattern index The landscape pattern index was calculated using the Fragstats4.2 software. The fol- lowing landscape pattern indices were selected for the landscape pattern analysis, including the number of patches (NP), the landscape shape index (LSI), area (AREA_MN), fractal dimension index (FRAC_MN), aggregation index (AI), contagion index (CONTIG_MN), and shape index (SHAPE_MN). These indices are correlated with the degree of fragmenta- tion, regularity, connectivity of the landscape type, and the degree of human disturbance, respectively [32,33]. (2) Global autocorrelation and LISA Global spatial autocorrelation was used for testing the distribution pattern of an element in the entire space, which can be divided into aggregation, discrete, and random. In this study, the global Moran index was used for reﬂecting the spatial correlation of different landscape processes in the study area [34]. This method is well known in the research ﬁeld and will not be elaborated. Local spatial autocorrelation involves the decomposition of the global spatial autocor- relation into each spatial unit. It reﬂects the spatial correlation between the attribute value of the elements and the adjacent elements in the entire area and a small local area [20]. It is expressed as follows: x x LISA = S W (x x), i 6= j i 2 i j i j=1 (2) n n 2 1 1 S = S (x x) , x = S x i i n n j=1 j=1 where S is the variance, W is the element of the spatial weight matrix, and x , x are the i j i j spatial units after row standardization. The range of the Moran index value of the local units is [1, 1]. According to the positive and negative LISA values, the spatial units can be divided into two types (namely, high-high and low-low), two types of positive correlation (namely, high-low and low-high), two types of negative correlation, and ﬁve types of insigniﬁcant correlation. 2.4.3. Analyzing the Driving Factors of the Landscape Processes: The OPGD Model in the R“GD” Package The OPGD model includes ﬁve parts: factor detector, parameter optimization, interac- tion detector, risk detector, and ecological detector. (1) Q-statistic As the core part of the geographical detector, the factor detector reveals the rela- tive importance of the explanatory variables with a Q-statistic. The Q-statistic compares Land 2022, 11, 7 7 of 23 the dispersion variances between observations in the entire study area and the strata of variables [24,25]. The Q-statistic is computed as follows: M 2 S N 1 s ( ) v,j j=1 v,j (3) Q = 1 (N 1)s v v where N and s are the number of observations and their variance, respectively, within v,j the entire study area, and N and s are the number of observations and their variance, v,j v,j respectively, within the jth (j = 1, . . . , M) subregion of the variable v. A large Q-statistic value implies the relatively high importance of the explanatory variable due to a large variance between the subregions. (2) Interaction Q-statistic The interaction detector determines the interactive impact of two overlapped spatial variables based on the relative importance of the interactions computed with the Q-statistic of the factor detector. The interaction detector explores an interaction by comparing the Q-statistic of the interaction with the two single variables. The interaction detector explores ﬁve interaction situations, including nonlinear weakening, univariable weakening, bivari- able enhancement, independent, and nonlinear enhancement, as listed in Table 3 [24,25]. (3) The risk mean in the OPGD model The risk mean is the mean statistic of the dependent variable attributes in different intervals of optimization of spatial discretization measured by the risk detector [26,28]. It can reﬂect the mean value of the dependent variable within the optimal interval and provide a basis for analyzing spatial heterogeneity. Table 3. Interactions between the two explanatory variables and their interactive impact. Geographical Interaction Relationship Interaction Nonlinear weakening: Impacts of single variables are nonlinearly Q < min(Q , Q ) u[ v u v weakened by the interaction of two variables. Univariable weakening: Impacts of single variables are univariable min(Q , Q ) Q max(Q , Q ) u v u\ v u v weakened by the interaction. Bivariable enhancement: Impact of single variables are bi-variable max(Q , Q ) < Q < (Q + Q ) u v u\ v u v enhanced by the interaction Q = (Q + Q ) Independent: Impacts of variables are independent. u\ v u v Q > (Q + Q ) Nonlinear enhancement: Impacts of variables are nonlinearly enhanced u\ v u v Q is the Q-statistic of the variable u, Q is the Q-statistic of the variable v, and Q is the Interact Q-statistic u v u\ v between the variables u and v. 3. Results 3.1. Overall Inﬂuence of the Landscape Process on the Quantity and Space of Different Landscape Types 3.1.1. The General Quantity and Distribution of the Different Landscape Types in Each Decade The number of landscape types in the study area in each decade is shown in Table 4. It can be seen that arable land, woodland, grassland, and built-up land are the main landscape types, which account for more than 90% of the total area of the region. Land 2022, 11, 7 8 of 23 Table 4. The number of landscape types in each decade. Landscape 1990a 2000a 2010a 2018a Types Area/ha Proportion Area/ha Proportion Area/ha Proportion Area/ha Proportion 1 393,809.58 61.93% 383,685.39 60.33% 387,605.25 60.73% 361,905.21 56.49% 2 48,124.35 7.57% 48,123.9 7.57% 41,474.61 6.50% 43,127.46 6.73% 3 65,403.36 10.29% 65,205.81 10.25% 19,870.92 3.11% 25,958.16 4.05% 4 25,633.08 4.03% 26,693.28 4.20% 27,732.06 4.35% 28,165.14 4.40% 5 26,351.37 4.14% 26,791.02 4.21% 20,321.28 3.18% 13,326.12 2.08% 6 75,513.78 11.88% 84,483.36 13.28% 140,744.79 22.05% 167,824.89 26.19% 7 1017.9 0.16% 1018.08 0.16% 456.93 0.07% 375.66 0.06% Total area 635,853.42 636,000.84 638,205.84 640,682.64 Arable land has always occupied the dominant position, from 1990 to 2018, spread over Jiaozhou District, Chengyang District, Jimo District, and Huangdao District. Woodland and grassland are mainly distributed in higher elevations of hills and mountains in Laoshan District, southern Jiaozhou district, and central Huangdao District. The wetlands and waters in the study area are mainly distributed in the north of Jiaozhou Bay, at the junction boundary of the administrative area of Jiaozhou District, Chengyang District, and Jimo district. The built-up land is located in the administrative center and around the bay (Figure 3). 3.1.2. Inﬂuence of the Landscape Process on the Quantity and Distribution of Different Landscape Types In the study period, the arable land, woodland, grassland, and unused land exhibited an overall shrinking trend, whereas a gradual expansion of the built-up land was observed. In the past 30 years, the arable land area decreased slightly, from 393,809.58 ha in 1990 to 361,905.21 ha in 2018, representing an overall proportion decrease of 5.45%. The period from 2010 to 2018 saw a loss of 25,700.04 ha of arable land. From 2000 to 2010, the area of woodland decreased signiﬁcantly, by an amount of 6649.29 ha. In the next decade, the area of woodland rose slightly. The grassland area was stable from 1990 to 2000. However, it decreased sharply from 65,205.81 ha to 19,870.92 ha in 2000 and 2010, respectively, with a decrease rate of nearly 70%. The grassland area recovered slightly from 2010 to 2018. The total amount of built-up land expanded from 75,513.78 ha in 1990 to 167,824.89 ha in 2018. The proportion of the regional area expanded from 11.88% in 1990 to 26.19% in 2018. The total area of built-up land increased from 84,483.36 ha between 2000 to 2010. In the past 30 years, the salt-ﬁeld and ﬁsh-farm have reduced from 26,351.37 ha in 1990 to 13,326.12 ha in 2018, with the total area reduced by nearly half. The unused land has decreased by half, and the wetland water area has increased steadily. Wetlands and waters have increased slightly from 25,633.08 ha in 1990 to 28,165.14 ha. Land 2022, 11, x 8 of 23 7 1017.9 0.16% 1018.08 0.16% 456.93 0.07% 375.66 0.06% Total area 635853.42 636000.84 638205.84 640682.64 Arable land has always occupied the dominant position, from 1990 to 2018, spread over Jiaozhou District, Chengyang District, Jimo District, and Huangdao District. Wood- land and grassland are mainly distributed in higher elevations of hills and mountains in Laoshan District, southern Jiaozhou district, and central Huangdao District. The wetlands and waters in the study area are mainly distributed in the north of Jiaozhou Bay, at the junction boundary of the administrative area of Jiaozhou District, Chengyang District, and Jimo district. The built-up land is located in the administrative center and around the bay (Figure 3). 3.1.2. Influence of the Landscape Process on the Quantity and Distribution of Different Landscape Types In the study period, the arable land, woodland, grassland, and unused land exhibited an overall shrinking trend, whereas a gradual expansion of the built-up land was ob- served. In the past 30 years, the arable land area decreased slightly, from 393809.58 ha in 1990 to 361905.21 ha in 2018, representing an overall proportion decrease of 5.45%. The period from 2010 to 2018 saw a loss of 25700.04 ha of arable land. From 2000 to 2010, the area of woodland decreased significantly, by an amount of 6649.29 ha. In the next decade, the area of woodland rose slightly. The grassland area was stable from 1990 to 2000. However, it decreased sharply from 65205.81 ha to 19870.92 ha in 2000 and 2010, respectively, with a decrease rate of nearly 70%. The grassland area recovered slightly from 2010 to 2018. The total amount of built-up land expanded from 75513.78 ha in 1990 to 167824.89 ha in 2018. The proportion of the regional area expanded from 11.88% in 1990 to 26.19% in 2018. The total area of built-up land increased from 84483.36 ha between 2000 to 2010. In the past 30 years, the salt-field and fish-farm have reduced from 26351.37 ha in 1990 to 13326.12 ha in 2018, with the total area reduced by nearly half. The unused land has de- Land 2022, 11, 7 9 of 23 creased by half, and the wetland water area has increased steadily. Wetlands and waters have increased slightly from 25633.08 ha in 1990 to 28165.14 ha. Figure 3. Spatial distribution of various types of landscape. (a) 1990, (b) 2000, (c) 2010, (d) 2018. Figure 3. Spatial distribution of various types of landscape. (a) 1990, (b) 2000, (c) 2010, (d) 2018. As can be seen from Figure 3, arable land was occupied by the expanding rural and urban construction land, showing the characteristics of spatial fragmentation and shrinkage. Most of Chengyang District, the middle of Jiaozhou district, and the northwest of Huangdao District were occupied by built-up land. The spatial characteristics of and the grassland occupied by arable land are signiﬁcant and are mainly concentrated in the south of Huangdao District and Jiaozhou district. Nevertheless, the spatial status of woodland and grassland in the eastern part of Chengyang District and Laoshan District were efﬁciently maintained. Wetlands and waters in the north of Jiaozhou Bay were increasingly occupied by the expansion of the built-up land. Salt-ﬁeld and ﬁsh-farm areas were shrinking, especially in the north of Jiaozhou Bay, and large parts of them were turned into built-up land. As can be seen in Figure 4, the mean centers generated by ArcGIS 10.3 of all landscape types showed a large migration distance between 2000 and 2010, except for salt-ﬁeld and ﬁsh-farm areas. This indicates that the landscape process was very dramatic between 2000 and 2010. The mean center of arable land and unused land moved to the northeast from 1990 to 2010 and folded back to the southwest. Combined with the quantitative and distribution characteristics of the above analysis, we found that the net area reduction in the southwest of the mean center was more than that in the northeast. It might be inﬂuenced by the landscape process of unbalanced inﬂow and outﬂow of the arable land or the unused land. Land 2022, 11, x 9 of 23 As can be seen from Figure 3, arable land was occupied by the expanding rural and urban construction land, showing the characteristics of spatial fragmentation and shrink- age. Most of Chengyang District, the middle of Jiaozhou district, and the northwest of Huangdao District were occupied by built-up land. The spatial characteristics of and the grassland occupied by arable land are significant and are mainly concentrated in the south of Huangdao District and Jiaozhou district. Nevertheless, the spatial status of woodland and grassland in the eastern part of Chengyang District and Laoshan District were effi- ciently maintained. Wetlands and waters in the north of Jiaozhou Bay were increasingly occupied by the expansion of the built-up land. Salt-field and fish-farm areas were shrink- ing, especially in the north of Jiaozhou Bay, and large parts of them were turned into built- up land. As can be seen in Figure 4, the mean centers generated by ArcGIS 10.3 of all landscape types showed a large migration distance between 2000 and 2010, except for salt-field and fish-farm areas. This indicates that the landscape process was very dramatic between 2000 and 2010. The mean center of arable land and unused land moved to the northeast from 1990 to 2010 and folded back to the southwest. Combined with the quantitative and distribu- tion characteristics of the above analysis, we found that the net area reduction in the south- Land 2022, 11, 7 10 of 23 west of the mean center was more than that in the northeast. It might be influenced by the landscape process of unbalanced inflow and outflow of the arable land or the unused land. Figure 4. Map showing the migration of the mean center of the different landscape types. Figure 4. Map showing the migration of the mean center of the different landscape types. The mean centers of the grassland and the woodland migrated eastward on the whole. The mean centers of the grassland and the woodland migrated eastward on the Combined with the above analysis, it can be observed that the woodland and the grassland whole. Combined with the above analysis, it can be observed that the woodland and the being present in the eastern hilly region affected the shift of the mean center. The largest grassland being present in the eastern hilly region affected the shift of the mean center. migration of the mean center was that of the grassland. It migrated 9480.334 m (3.669 ) to the north and 16,083.74 m (11.746 ) to the east in general. The mean center of the wetlands and waters and the built-up land migrated to the southwest. The built-up land increased considerably, but the mean center of the construc- tion land migrated slightly to the southwest. It moved by a smaller distance of 838.615 m to the south and 1186.41 m to the west. 3.2. Quantity and Spatial Distribution of Landscape Processes in Each Decade 3.2.1. Rate of Change of the Various Landscape Processes As can be seen in Figure 5, the rate of change of the percentage of built-up land, grassland, unused land, and salt-ﬁeld and ﬁsh-farm areas in the study area was relatively fast, whereas the rate of change of the percentage of arable land, woodland, and wetlands and waters was relatively slow. The comprehensive dynamic attitude from 2000 to 2010 was the largest, whereas it was small from 1990 to 2000. Its code on the horizontal axis is 8 in Figure 5. Land 2022, 11, x 10 of 23 The largest migration of the mean center was that of the grassland. It migrated 9480.334 m (3.669′) to the north and 16083.74 m (11.746′) to the east in general. The mean center of the wetlands and waters and the built-up land migrated to the southwest. The built-up land increased considerably, but the mean center of the construc- tion land migrated slightly to the southwest. It moved by a smaller distance of 838.615 m to the south and 1186.41 m to the west. 3.2. Quantity and Spatial Distribution of Landscape Processes in Each Decade 3.2.1. Rate of Change of the Various Landscape Processes As can be seen in Figure 5, the rate of change of the percentage of built-up land, grassland, unused land, and salt-field and fish-farm areas in the study area was relatively fast, whereas the rate of change of the percentage of arable land, woodland, and wetlands and waters was relatively slow. The comprehensive dynamic attitude from 2000 to 2010 Land 2022, 11, 7 11 of 23 was the largest, whereas it was small from 1990 to 2000. Its code on the horizontal axis is 8 in Figure 5. 8.00% 6.00% 4.00% 2.00% 0.00% 123 4567 8 -2.00% -4.00% -6.00% -8.00% 1990-2000 2000-2010 2010-2018 1990-2018 Figure 5. Single and comprehensive dynamic attitude of the landscape processes in each decade. Figure 5. Single and comprehensive dynamic attitude of the landscape processes in each decade. Built-up land always showed a fast growth rate in all periods. The percentage of the Built-up land always showed a fast growth rate in all periods. The percentage of the built-up land, woodland, grassland, and unused land changed dramatically from 2000 to built-up land, woodland, grassland, and unused land changed dramatically from 2000 to 2010; 2010; the the v values alues of of which which wer wee re 6.66%, 6.66% , −1.38%, 1.38%, −6.95 6.95%, %, a and nd − 5. 5.51%, 51%, re respectively spectively. . Fr From om 2000 2000 t to o 2010, 2010woodland , woodland and angrassland d grasslanshowed d showed ra rapid pi negative d negatigr ve growt owth, exhibiting h, exhibit- aidynamic ng a dyna attitude mic attitude of of 1.38% −1.38% and and 6.95%, −6.95% respectively , respecti.vFr ely. From om 2010 2to 010 to 20 2018, the 18, th woodland e wood- and land the and grassland the grassl showed and showed rap rapid positive id posi gr tiv owth, e growt with h, wi dynamic th dynattitudes amic attitud of 0.50% es of 0and .50% 3.83%, respectively. The direction of growth also changed. and 3.83%, respectively. The direction of growth also changed. 3.2.2. Quantity Relation and Spatial Distribution of the Landscape Processes in 3.2.2. Quantity Relation and Spatial Distribution of the Landscape Processes in Each Each Decade Decade There were five major conversion relationships during 1990–2000, as shown in Figure 6a. There were five major conversion relationships during 1990–2000, as shown in Figure Arable land and built-up land are the core landscape types of the landscape process. The 6a. Arable land and built-up land are the core landscape types of the landscape process. conversion quantity from arable land to built-up land accounted for 82.70% of the transfer The conversion quantity from arable land to built-up land accounted for 82.70% of the area. Its area was the largest, reaching 8954.64 ha, followed by the landscape process of arable transfer area. Its area was the largest, reaching 8954.64 ha, followed by the landscape pro- land to wetlands and waters, at 1084.77 ha. Major conversion relationships did not include cess of arable land to wetlands and waters, at 1084.77 ha. Major conversion relationships woodland types during this period. did not include woodland types during this period. Eight major conversion relationships accounted for 94.72% of the total conversion area between 2000 and 2010 (Figure 6b). The proportion of arable land to built-up land was still the highest, at 37.34%, followed by that of grassland to arable land, at 28.28%. Built-up land to arable land was another main relationship, accounting for 9.07%. In this period, the outﬂow of arable land and its inﬂow achieved equilibrium. Built-up land and grassland became sources of cultivated land. Woodland, built-up land, and grassland contributed 6545.07 ha, 12,216.87 ha, and 38,077.74 ha to the arable land. The total conversion area of the three was 56,839.68 ha. There was a signiﬁcant net increase of 55,359.54 ha in the built- up land, whereas arable land contributed 50,283.54 ha. The above quantitative relations guarantee the tremendous growth of the built-up land without reducing the amount of cultivated land. There were 14 major landscape processes between 2010 and 2018 (Figure 6c). There were mutual conversion relationships between landscape types, such as cultivated land and forest land, cultivated land and construction land, cultivated land and wetland and waters area, and forest land and grassland. Conversion of the arable land to the built-up land was still dominant, about 22,155.12 ha, accounting for 39.86%. Salt-ﬁeld and ﬁsh-farm Land 2022, 11, x 11 of 23 Eight major conversion relationships accounted for 94.72% of the total conversion area between 2000 and 2010 (Figure 6b). The proportion of arable land to built-up land was still the highest, at 37.34%, followed by that of grassland to arable land, at 28.28%. Built-up land to arable land was another main relationship, accounting for 9.07%. In this period, the outflow of arable land and its inflow achieved equilibrium. Built-up land and grassland became sources of cultivated land. Woodland, built-up land, and grassland con- tributed 6545.07 ha, 12216.87 ha, and 38077.74 ha to the arable land. The total conversion area of the three was 56839.68 ha. There was a significant net increase of 55,359.54 ha in the built-up land, whereas arable land contributed 50283.54 ha. The above quantitative relations guarantee the tremendous growth of the built-up land without reducing the amount of cultivated land. There were 14 major landscape processes between 2010 and 2018 (Figure 6c). There were mutual conversion relationships between landscape types, such as cultivated land and forest land, cultivated land and construction land, cultivated land and wetland and waters area, and forest land and grassland. Conversion of the arable land to the built-up Land 2022, 11, 7 12 of 23 land was still dominant, about 22155.12 ha, accounting for 39.86%. Salt-field and fish-farm areas of approximately 6032.34 ha became one of the sources of built-up land, accounting for 10.85% of the transfer area. areas of approximately 6032.34 ha became one of the sources of built-up land, accounting During this period, the inflow of cultivated land was not enough to compensate for for 10.85% of the transfer area. its outflow. The net decrease in the cultivated land from 2010 to 2018 was 24,239.07 ha. During this period, the inﬂow of cultivated land was not enough to compensate for its The grassland did not compensate for the arable land, and its amount did not exceed 1% outﬂow. The net decrease in the cultivated land from 2010 to 2018 was 24,239.07 ha. The of the total proportion. The other main supplementary channels have provided only grassland did not compensate for the arable land, and its amount did not exceed 1% of the 8607.06 ha of arable land. However, the area of the arable land transferred out to the built- total proportion. The other main supplementary channels have provided only 8607.06 ha up land, grassland, wetlands and waters, and woodland was 22155.12 ha, 5375.25 ha, of arable land. However, the area of the arable land transferred out to the built-up land, 2959.83 ha, and 2355.93 ha, respectively. The total area of arable land transferred out was grassland, wetlands and waters, and woodland was 22,155.12 ha, 5375.25 ha, 2959.83 ha, 32846.13 ha. and 2355.93 ha, respectively. The total area of arable land transferred out was 32,846.13 ha. 4.86% 28.28% 2 1 3 2 1 2.33% 10.03% 82.70% 1.55% 1.56% 37.34% 9.07% 4.63% 4 6 5 4 6 5 2.76% 2.26%) 5.95% a.In 1990-2000 b.In 2000-2010 1.49% 1.56% 3.46% 20.54% 1.12% 9.67% 2 1 3 2 1 3 3.10% 4.24% 1.76% 9.04% 5.33% 1.71% 3.25% 41.94% 6.64% 4.27% 39.86% 1.29% 4 6 5 5 6 4 2.69% 7.07% 10.85% 3.62% 2.27% d.In 1990-2018 c.In 2010-2018 Figure 6. Figure 6. Prop Proportion ortion of of landscape proce landscape processes sses to to overall overalpr l proc ocesses essein s in ea eachch d decade. ecade (a . ( ) a During ) During t the h period e pe- riod 1990–2000; (b) During the period 2000–2010; (c) During the period 2010–2018; (d) During the 1990–2000; (b) During the period 2000–2010; (c) During the period 2010–2018; (d) During the period period 1990–2018. 1990–2018. In the recent 30 years, from 1990 to 2018, there were nine major transformation rela- In the recent 30 years, from 1990 to 2018, there were nine major transformation rela- tionships, accounting for 92.95%, and the 33 other transformation relationships accounted tionships, accounting for 92.95%, and the 33 other transformation relationships accounted for 7.05% of the area (Figure 6d). Among them, the transformation of arable land to built-up for 7.05% of the area (Figure 6d). Among them, the transformation of arable land to built- land, grassland to arable land, built-up land to arable land, and salt-ﬁeld and ﬁsh-farm to up land, grassland to arable land, built-up land to arable land, and salt-field and fish-farm built-up land accounted for 41.94%, 20.54%, 6.64%, and 7.07% of the total area, respectively. Their areas were 75,587.11 ha, 37,016.19 ha, 11,936.7 ha, and 12,749.49 ha, respectively. The landscape process of arable land to built-up land was distributed around the administrative center in Jimo, Jiaozhou, and the Chengyang District center. This process showed a diffuse distribution. The spatial distribution of landscape processes is shown in Figure 7. In 2000–2010, a large amount of grassland was transformed into cultivated land in the hilly region in the south of Jiaozhou District and the northwest of Huangdao District. In 2000–2018, salt-ﬁeld and ﬁsh-farm and wetlands and waters near Jiaozhou Bay, Dagu River, Taoyuan River, and Yanghe River were transformed into construction land. The process of arable land outﬂowing to wetland, forest land, and grassland was scattered in various districts and counties. Salt-ﬁeld and ﬁsh-farm areas to built-up land presented the state as a concentrated distribution of blocks. The conversion from built-up land to arable land ringed around the rural residential areas. The shape of the woodland and grassland to arable land contour lines along the hills and valleys, like branches, in Huangdao and Chengyang. Land 2022, 11, x 12 of 23 to built-up land accounted for 41.94%, 20.54%, 6.64%, and 7.07% of the total area, respec- tively. Their areas were 75,587.11 ha, 37016.19 ha, 11,936.7 ha, and 12,749.49 ha, respec- tively. The landscape process of arable land to built-up land was distributed around the administrative center in Jimo, Jiaozhou, and the Chengyang District center. This process Land 2022, 11, 7 13 of 23 showed a diffuse distribution. The spatial distribution of landscape processes is shown in Figure 7. Figure 7. Spatial distribution of the various landscape processes in each decade. (a) During the Figure 7. Spatial distribution of the various landscape processes in each decade. (a) During the period 1990–2000; (b) During the period 2000–2010; (c) During the period 2010–2018; (d) During period 1990–2000; (b) During the period 2000–2010; (c) During the period 2010–2018; (d) During the the period 2010–2018. period 2010–2018. In 2000–2010, a large amount of grassland was transformed into cultivated land in 3.2.3. Quantitative and Spatial Distribution Characteristics of All the Landscape Processes the hilly region in the south of Jiaozhou District and the northwest of Huangdao District. Through LCTA, we found that 15 transformation trajectories constitute the main In 2000–2018, salt-field and fish-farm and wetlands and waters near Jiaozhou Bay, Dagu landscape processes, which can be categorized into a landscape process of eight categories, River, Taoyuan River, and Yanghe River were transformed into construction land. The as provided in Table 5. As long as the landscape processes take place in each decade, the process of arable land outflowing to wetland, forest land, and grassland was scattered in entire landscape process will be classiﬁed into one of the eight categories. Therefore, for various districts and counties. example, the “1161” type belongs to both the outﬂow and inﬂow of arable land, as well as Salt-field and fish-farm areas to built-up land presented the state as a concentrated the landscape process of backﬂow of arable land. distribution of blocks. The conversion from built-up land to arable land ringed around the rural residential areas. The shape of the woodland and grassland to arable land contour Table 5. Eight types of landscape processes based on the changes in the landscape trajectories. lines along the hills and valleys, like branches, in Huangdao and Chengyang. Serial Number Name Main Trajectory Types 3.2.3. Quantitative and Spatial Distribution Characteristics of All the Landscape Pro- 1 Outﬂow of built-up land 6611, 6661, 1161 cesses 2 Inﬂow of built-up land 5556, 5566, 1116, 1166, 1666, 3366, 4466 Through LCTA, we found that 15 transformation trajectories constitute the main 3 Outﬂow of ecological land 3311, 2211, 3366, 4466 landscape processes, which can be categorized into a landscape process of eight catego- 4 Inﬂow of ecological land 1144, 1114, 1113 ries, as provided in Table 5. As long as the landscape processes take place in each decade, 5 Outﬂow of arable land 1116, 1666, 1144, 1161, 1114, 1113, 1166 6 the entire lan Inﬂow dscape of proce arable ss w land ill be classified into on1161, e of the 3361, eight 2211, categor 6611, 6661 ies. Therefore, 7 Outﬂow of salt-ﬁeld and ﬁsh-farm 5556, 5566 for example, the “1161” type belongs to both the outflow and inflow of arable land, as well 8 Backﬂow of arable land 1161 as the landscape process of backflow of arable land. From the number of various landscape processes, we can see that the transfer trajectory had two directions. One was the inﬂow of the built-up land from arable land, grassland, salt- ﬁeld and ﬁsh-farm, and the other part was the outﬂow of the arable land to the grassland and the built-up land. Among the 15 landscape types, the number of the “1116” trajectories was the highest, followed by “3311”, and “1116”, which corresponded to 47,245.32 ha, 36,198.27 ha, and 19,443.78 ha, respectively (Figure 8). The distribution characteristics of the landscape processes were similar to those in the previous section and thus will not be elaborated on here. Land 2022, 11, x 13 of 23 Table 5. Eight types of landscape processes based on the changes in the landscape trajectories. Serial Number Name Main Trajectory Types 1 Outflow of built-up land 6611, 6661, 1161 2 Inflow of built-up land 5556, 5566, 1116, 1166, 1666, 3366, 4466 3 Outflow of ecological land 3311, 2211, 3366, 4466 4 Inflow of ecological land 1144, 1114, 1113 5 Outflow of arable land 1116, 1666, 1144, 1161, 1114, 1113, 1166 6 Inflow of arable land 1161, 3361, 2211, 6611, 6661 7 Outflow of salt-field and fish-farm 5556, 5566 8 Backflow of arable land 1161 From the number of various landscape processes, we can see that the transfer trajec- tory had two directions. One was the inflow of the built-up land from arable land, grass- land, salt-field and fish-farm, and the other part was the outflow of the arable land to the grassland and the built-up land. Among the 15 landscape types, the number of the “1116” trajectories was the highest, followed by “3311”, and “1116”, which corresponded to 47245.32 ha, 36198.27 ha, and 19443.78 ha, respectively (Figure 8). The distribution char- Land 2022, 11, 7 14 of 23 acteristics of the landscape processes were similar to those in the previous section and thus will not be elaborated on here. Figure 8. Map showing the changes in the trajectories of the landscape processes. Figure 8. Map showing the changes in the trajectories of the landscape processes. 3.3. 3.3. Spatial Spatial Di Distribution stributionHeter Heterogen ogeneity eity of the La of the Landscape ndscape Pr Processes ocesses 3.3.1. Dynamic Pattern of the Landscape Processes Revealed by the Landscape 3.3.1. Dynamic Pattern of the Landscape Processes Revealed by the Landscape Pattern Pattern Index Index The NP index and AREA_MN index reﬂect the number of patches and the average The NP index and AREA_MN index reflect the number of patches and the average area of patches in the landscape. It is worth noting that the ecological outﬂow landscape area of patches in the landscape. It is worth noting that the ecological outflow landscape process has the largest number of patches. The average patch area is the smallest in the process has the largest number of patches. The average patch area is the smallest in the process of backﬂow of the arable land. The outﬂow and inﬂow of cultivated land and process of backflow of the arable land. The outflow and inflow of cultivated land and built-up land have many landscape patches. The NP index values are 2155, 2264, 2019, and built-up land have many landscape patches. The NP index values are 2155, 2264, 2019, 1959, respectively, indicating the existence of many patches in these landscape processes. The average patch area in the process of built-up land inﬂow is large. Its AREA_MN index is 50.3849. However, the AREA_MN index of the outﬂow of built-up land is 7.2452 (Table 6). The SHAPE_MN and FRAC_MN indices measure the shape regularity of the patches, whereas LSI measures the degree of overall regularity at the landscape level. The SHAPE_MN index of outﬂow of the ecological landscape is 1.4379, indicating that the patches are irregu- lar in shape. Similarly, its LSI is 42.1911, indicating that its patches and overall landscape process are irregular. The SHAPE_MN index of the processes of built-up land inﬂow, ecological land inﬂow, and arable land backﬂow is 1.0661, 1.093, and 1.0141, respectively, indicating that the patches of the above three landscape processes are relatively regu- lar. Similarly, ecological land inﬂow and arable land backﬂow are more regular on the landscape scale because their LSI index values are small, namely, 23.0505 and 24.8846, respectively. However, the LSI index of the built-up land inﬂow is large, indicating that its overall shape in the landscape is irregular. The AI index and CONTIG_MN index of the ecological land outﬂow, arable land outﬂow, arable land inﬂow were all high, indicating that the degree of patch connectivity and the degree of aggregation of the landscape processes are high. The CONTIG_MN landscape index of the process of ecological inﬂow landscape is 0.0604, but its AI index is 54.129. Although the CONTIG_MN is not high, its high AI index indicates that the overall degree of aggregation of this kind of landscape process is high, but the landscape connectivity within each patch is poor. The CONTIG_MN index of the landscape processes Land 2022, 11, 7 15 of 23 of built-up land outﬂow and arable land backﬂow is 0.0758 and 0.0123, respectively, and the AI index is 19.3243 and 4.4615, respectively (Table 6). The degree of connectivity in these patch types is not good, and the overall distribution and degree of aggregation of the landscape processes are not high. The outﬂow of salt-ﬁeld and ﬁsh-farm areas is a more speciﬁc landscape process. The outﬂow of salt-ﬁeld and ﬁsh-farm areas has the minimum NP and LSI index values and maximum AREA_MN, SHAPE_MN index values. The statistics are 28, 7.708, 449.2857, and 1.588, respectively, indicating that there are fewer patches but that they are large patches. The overall landscape shape is relatively regular, but the landscape patches are not. In terms of the degree of aggregation and connectivity, the CONTIG_MN index and the AI index are 0.5444 and 87.7287, respectively (Table 6). This indicates that the patches have good connectivity, and the aggregation in the overall landscape process is high. Table 6. Landscape pattern index of the landscape process. TYPE NP LSI AREA_MN SHAPE_MN FRAC_MN CONTIG_MN AI 1 2019 48.9587 7.2452 1.0661 1.014 0.0758 19.3243 2 1959 46.1746 50.3849 1.1674 1.0234 0.1398 70.9811 3 726 42.1911 69.5813 1.4379 1.0487 0.2873 62.9769 4 542 23.0505 17.9262 1.093 1.0125 0.0604 54.129 5 2155 49.1554 40.4492 1.1528 1.0215 0.1231 67.0717 6 2264 56.6429 24.9435 1.1782 1.0238 0.1275 52.7002 7 28 7.708 449.2857 1.588 1.0566 0.5444 87.7287 8 600 24.8846 4.5067 1.0141 1.0031 0.0123 4.4615 3.3.2. Spatial Heterogeneity of the Various Landscape Processes Analyzed Using Spatial Autocorrelation Using the GeoDa1.12 software, the global Moran index scatter plots were obtained. All the results passed the hypothetical test and could be used for further analysis. The Moran index values of the eight landscape processes are 0.149, 0.488, 0.465, 0.25, 0.454, 0.503, 0.550, and 0.016, respectively. Among ﬁve landscape processes, the Moran index is relatively high, and there are more “high-high” and “low-low” aggregations. This shows that the landscape processes are more concentrated in space. Further, the local Moran analysis is meaningful for them. However, the other landscape processes are relatively scattered in space. The information of the spatial relevance between the adjacent area could not be incorporated in the global Moran index. Therefore, in this study, further analysis of the local indicator of spatial association obtained from the LISA cluster diagram (Figure 9) was performed. The eight pictures correspond to the eight landscape processes in Table 5. As can be seen from Figure 9a, the “high-high” aggregation of the outﬂow of built-up land is concentrated in the suburbs and rural areas of Jimo, Jiaozhou, and Huangdao districts, in the suburban and rural areas far from the coast. Its distributions are intertwined with the “low-high” aggregation type. About 2274 grid cells exhibit “high-high” aggrega- tion, which is relatively dispersed, and 6859 grid cells exhibit the “low-high” aggregation state and are relatively concentrated. As is seen from Figure 9b, the “high-high” aggregation of inﬂow of built-up land is concentrated around Jiaozhou Bay, the administrative center, the Huangdao economic development zone, and the Hongdao high-tech zone. Further, 19,909 grid units exhibit a “high-high” aggregation state. The “high-high” aggregation of ecological land outﬂow is concentrated in the hilly area west of Jiaozhou Bay with a relatively high elevation and exhibits a fragmented distribution (Figure 9c). Further, 10,687 grid units exhibit a “high-high” aggregation. At the same time, the “high-high” aggregation in the landscape process of inﬂow of the ecological land is mainly distributed along the periphery of the Taoyuan and Dagu rivers (Figure 9d). Land 2022, 11, x 15 of 23 analysis is meaningful for them. However, the other landscape processes are relatively scattered in space. The information of the spatial relevance between the adjacent area could not be in- corporated in the global Moran index. Therefore, in this study, further analysis of the local indicator of spatial association obtained from the LISA cluster diagram (Figure 9) was Land 2022, 11, 7 16 of 23 performed. The eight pictures correspond to the eight landscape processes in Table 5. Figure 9. LISA cluster diagram of the different landscape processes. (a) Outﬂow of built-up land; Figure 9. LISA cluster diagram of the different landscape processes. (a) Outflow of built-up land; (b) Inﬂow of built-up land; (c) Outﬂow of ecological land; (d) Inﬂow of ecological land; (e) Outﬂow (b) Inflow of built-up land; (c) Outflow of ecological land; (d) Inflow of ecological land; (e) Outflow of arable land; (f) Inflow of arable land; (g) Outflow of salt-field and fish-farm; (h) Backflow of arable of arable land; (f) Inﬂow of arable land; (g) Outﬂow of salt-ﬁeld and ﬁsh-farm; (h) Backﬂow of land. arable land. As c In the an be se outﬂow en from F of arable igu land re 9a (Figur , the “hi e 9e), gh-high the “high-high” ” aggregation o aggrfegation the outflow is distributed of built- around the periphery of Jiaozhou and Jimo administrative center, with 17,119 grid units. up land is concentrated in the suburbs and rural areas of Jimo, Jiaozhou, and Huangdao Large areas of arable land around the city have been occupied. It is clear from Figure 9f districts, in the suburban and rural areas far from the coast. Its distributions are inter- that the “high-high” aggregation of arable land outﬂow is distributed in the hilly areas twined with the “low-high” aggregation type. About 2274 grid cells exhibit “high-high” west of Jiaozhou Bay and the rural areas in Huangdao and Jimo, interweaving with the aggregation, which is relatively dispersed, and 6859 grid cells exhibit the “low-high” ag- “low-high” aggregated grid units. Reclamation of rural residential areas and development gregation state and are relatively concentrated. As is seen from Figure 9b, the “high-high” of grassland might be able to explain this spatial distribution. aggregation of inflow of built-up land is concentrated around Jiaozhou Bay, the adminis- The outﬂow of salt-ﬁeld and ﬁsh-farm is distributed along the coast of Jiaozhou Bay, trative center, the Huangdao economic development zone, and the Hongdao high-tech in the Hongdao high-tech zone and Dongjiakon Port (Figure 9g). Local indicators of spatial zone. Further, 19,909 grid units exhibit a “high-high” aggregation state. association are not signiﬁcant in the backﬂow of arable land in the study area (Figure 9h). The “high-high” aggregation of ecological land outflow is concentrated in the hilly area west of Jiaozhou Bay with a relatively high elevation and exhibits a fragmented dis- 3.4. Factors Driving the Spatial Heterogeneity of the Various Landscape Processes Obtained Using tribution (Figure 9c). Further, 10,687 grid units exhibit a “high-high” aggregation. At the the OPGD Model same time, the “high-high” aggregation in the landscape process of inflow of the ecologi- 3.4.1. Important Inﬂuencing Factors Revealed by Q-Statistic cal land is mainly distributed along the periphery of the Taoyuan and Dagu rivers (Figure The results of the factor detector and interaction detector are shown in Figures 10 and 11 9d). from, which the following is observed: X10 has the greatest influence on the five landscape In the outflow of arable land (Figure 9e), the “high-high” aggregation is distributed processes except for the outflow of salt-field and breeding area. X6, X7, and X8 also exhibit a around the periphery of Jiaozhou and Jimo administrative center, with 17,119 grid units. huge impact on the different landscape processes. The abovementioned driving factors are all Large areas of arable land around the city have been occupied. It is clear from Figure 9f over 0.1. However, X2 and X12 have no more than 0.1 influence factors on the five landscape that the “high-high” aggregation of arable land outflow is distributed in the hilly areas processes. X1 and X11 have a smaller influence factor, close to 0.1. Factors were selected as in west of Jiaozhou Bay and the rural areas in Huangdao and Jimo, interweaving with the Table 2. “low-high” aggregated grid units. Reclamation of rural residential areas and development From the interactive detector results shown in Figure 11, it can be observed that the of grassland might be able to explain this spatial distribution. interaction between the Q-statistic is shown as bivariable-enhance or nonlinear-enhance; there is no independent or weakened state. The interaction between the different factors has different degrees of enhancement. It is also conﬁrmed that the landscape process is a kind of complex factor interaction process. Land 2022, 11, x 16 of 23 The outflow of salt-field and fish-farm is distributed along the coast of Jiaozhou Bay, in the Hongdao high-tech zone and Dongjiakon Port (Figure 9g). Local indicators of spa- tial association are not significant in the backflow of arable land in the study area (Figure 9h). 3.4. Factors Driving the Spatial Heterogeneity of the Various Landscape Processes Obtained Using the OPGD Model 3.4.1. Important Influencing Factors Revealed by Q-Statistic The results of the factor detector and interaction detector are shown in Figure 10 and Figure 11 from, which the following is observed: X10 has the greatest influence on the five landscape processes except for the outflow of salt-field and breeding area. X6, X7, and X8 also exhibit a huge impact on the different landscape processes. The abovementioned driving factors are all over 0.1. However, X2 and X12 have no more than 0.1 influence Land 2022, 11, 7 17 of 23 factors on the five landscape processes. X1 and X11 have a smaller influence factor, close to 0.1. Factors were selected as in Table 2. Q-statistic 0.8 0.6 0.4 0.2 12 34 56 78 9 10 11 12 Q2 Q3 Q5 Q6 Q7 Figure 10. Q-statistic for each landscape process. (Q2, Q3, Q5, Q6, Q7 corresponding to the serial Figure 10. Q-statistic for each landscape process. (Q2, Q3, Q5, Q6, Q7 corresponding to the serial number number of landscape processes 2,3 of landscape processes 2,3,4,6,7 in,4,6 Table ,7 in T 5; Abscissa able 5;1-12 Abcorr scisesponding sa 1-12 correspo to the data nding number to the data number in in Table Table 2). 2). 3.4.2. Results of the Geographical Detector for the Major Landscape Processes From the interactive detector results shown in Figure 11, it can be observed that the (1) Inﬂow of built-up land interaction between the Q-statistic is shown as bivariable-enhance or nonlinear-enhance; The order of the signiﬁcant Q-statistic (value > 0.1) corresponding to this landscape there is no independent or weakened state. The interaction between the different factors process is as follows: X10 > X8 > X6 > X7 > X9 > X3 (Figure 10). The variables with a has different degrees of enhancement. It is also confirmed that the landscape process is a strong inﬂuence (Q-statistic greater than 0.2) are X10, X8, and X6, respectively. In this kind of complex factor interaction process. landscape process, the interactions between X1, X2, X3, X4, and X7, X8, X9 are nonlinear- enhancement. In addition, the interactions between X5, X7, X10, X11, and other factors are also non-linear enhancement (Figure 11). This indicates that the interaction statistics are 3.4.2. Results of the Geographical Detector for the Major Landscape Processes larger than the sum of the two Q-statistics. The variable of X10 and other variables jointly (1) Inflow of built-up land promote the landscape process greatly. The largest interaction variables are X8 and X10, The order of the significant Q-statistic (value > 0.1) corresponding to this landscape and the interaction statistic is 0.4527. This value is greater than the maximum Q-statistic of X8 and X10. process is as follows: X10 > X8 > X6 > X7 > X9 > X3 (Figure 10). The variables with a strong By comparing the values of the risk mean, the following was observed. For the inﬂow influence (Q-statistic greater than 0.2) are X10, X8, and X6, respectively. In this landscape of built-up land, the risk mean attribute values in different ecological landscape types are process, the interactions between X1, X2, X3, X4, and X7, X8, X9 are nonlinear-enhance- different. For X10, the risk mean in nonecological landscape types is 0.75. For lakes, tidal ment. In addition, the interactions between X5, X7, X10, X11, and other factors are also ﬂats, and reservoirs, the landscape process attribute value is approximately equal to 0.9. For non-linear enhancement (Figure 11). This indicates that the interaction statistics are larger woodland, the attribute value is 0. This indicates that large amounts of inﬂow of built-up land are distributed in wetlands and waters, whereas the woodland is not converted to than the sum of the two Q-statistics. The variable of X10 and other variables jointly pro- built-up land. For X8, the risk mean value is large in the two intervals (0, 2180), (4400, mote the landscape process greatly. The largest interaction variables are X8 and X10, and 61,200). These values are more than 0.6 or even close to 0.9. In X3, X6, X7, the higher the the interaction statistic is 0.4527. This value is greater than the maximum Q-statistic of X8 interval value, the lower the risk mean value. This shows that the farther away from the and X10. coast, the higher the elevation and vegetation coverage, and the inﬂow of the built-up land is less. Land 2022, 11, x 17 of 23 By comparing the values of the risk mean, the following was observed. For the inflow of built-up land, the risk mean attribute values in different ecological landscape types are different. For X10, the risk mean in nonecological landscape types is 0.75. For lakes, tidal flats, and reservoirs, the landscape process attribute value is approximately equal to 0.9. For woodland, the attribute value is 0. This indicates that large amounts of inflow of built- up land are distributed in wetlands and waters, whereas the woodland is not converted to built-up land. For X8, the risk mean value is large in the two intervals (0, 2180), (4400, 61200). These values are more than 0.6 or even close to 0.9. In X3, X6, X7, the higher the interval value, the lower the risk mean value. This shows that the farther away from the Land 2022, 11, 7 18 of 23 coast, the higher the elevation and vegetation coverage, and the inflow of the built-up land is less. Figure 11. Interactions between the two explanatory variables and their interactive statistic. (a) In- Figure 11. Interactions between the two explanatory variables and their interactive statistic. (a) Inter- teraction Q-statistic of inflow of built-up land; (b) interaction Q-statistic of outflow of ecological action Q-statistic of inﬂow of built-up land; (b) interaction Q-statistic of outﬂow of ecological land; land; (c) interaction Q-statistic of outflow of arable land; (d) interaction Q-statistic of inflow of arable (c) interaction Q-statistic of outﬂow of arable land; (d) interaction Q-statistic of inﬂow of arable land; land; (e) interaction Q-statistic of outflow of salt-field and fish farm areas. (e) interaction Q-statistic of outﬂow of salt-ﬁeld and ﬁsh farm areas. (2) Outﬂow of ecological land (2) Outflow of ecological land The order of the signiﬁcant Q-statistic (value > 0.1) for this landscape process is as The order of the significant Q-statistic (value > 0.1) for this landscape process is as follows: X10 > X6 > X5 > X8 > X7 > X4 (Figure 10). Similar to the case of the inﬂow of follows: X10 > X6 > X5 > X8 > X7 > X4 (Figure 10). Similar to the case of the inflow of built- built-up land, X10, X6, X8, and X7 are the major factors. However, X4 and X5 are important up land, X10, X6, X8, and X7 are the major factors. However, X4 and X5 are important inﬂuencing factors; an observation which is contrary to that for the inﬂow of built-up land. influencing factors; an observation which is contrary to that for the inflow of built-up land. The factors with strong explanatory power (Q-statistic greater than 0.2) are X10 and X6. The factors with strong explanatory power (Q-statistic greater than 0.2) are X10 and X6. In this landscape process, the interactions between X1, X2, X3 and X2, X3, X4, X6, X7, X8, In this landscape process, the interactions between X1, X2, X3 and X2, X3, X4, X6, X7, X8, X9, X11, X12 are almost nonlinear-enhance (Figure 11). The interactions between X4 and X9, X11, X12 are almost nonlinear-enhance (Figure 11). The interactions between X4 and X7, X8, X9 as well as the interactions between X8 and X9, X10 are also nonlinear-enhance. X7, X8, X9 as well as the interactions between X8 and X9, X10 are also nonlinear-enhance. The interactions corresponding to X10 are signiﬁcant in the interaction statistic matrix. The The interactions corresponding to X10 are significant in the interaction statistic matrix. type of ecological landscape greatly promotes the landscape process. The four distance variables The type o enhance f ecologic others al on lanthe dscape greatly pr formation of ecological omotes the landscape process. The fo land outﬂow. ur dis- tancBy e variables enh comparing the ance o risk mean, thers on thethe followi formation o ng was observed. f ecologicFor al land X10,outflow the mean . risk of the landscape process in the nonecological landscape types is 0. Except for 41 and 46 in By comparing the risk mean, the following was observed. For X10, the mean risk of the CNLUCC, the mean risk values of the other types are greater than 0.95. The outﬂow the landscape process in the nonecological landscape types is 0. Except for 41 and 46 in of ecological land is generally distributed in the ecological landscape types, however, not the CNLUCC, the mean risk values of the other types are greater than 0.95. The outflow in Graff and Bottomland. In X6, X5, X7, the higher the interval value, the higher is the of ecological land is generally distributed in the ecological landscape types, however, not mean risk value. The minimum and maximum values of the interval are 0.17 and 0.53 for in Graff and Bottomland. In X6, X5, X7, the higher the interval value, the higher is the X7, 0.09 and 0.82 for X6, and 0.14 and 0.78 for X5, respectively. This shows that the higher mean risk value. The minimum and maximum values of the interval are 0.17 and 0.53 for the elevation, the higher is the vegetation coverage, and the greater is the slope, which X7, 0.09 and 0.82 for X6, and 0.14 and 0.78 for X5, respectively. This shows that the higher leads to the outﬂow of the ecological landscape. In X4, the higher value of the landscape process is concentrated in the range of (3160, 13,400), and its value is about 0.5, indicating considerable outﬂow of the ecological landscape in the range of 3160 m to 13,400 m from the central town, i.e., in the suburban region. In X9, it is clear that the population increase will promote the outﬂow of the ecological landscape, but the process occurs less in regions with excessively high population growth. (3) Outﬂow of arable land The order of the significant Q-statistic (value > 0.1) is as follows: X10 > X8 > X6 > X7 > X4. The variables with strong influence (value > 0.2) are X10, X8, and X6, respectively. The main Land 2022, 11, 7 19 of 23 factors influencing the arable land outflow and built-up land inflow into the landscape are similar, but X4 is an important influencing factor corresponding to farmland outflow. In this landscape process, the interactions between X1, X2, X3, X4, and X6, X7, X8, X9, X11, X12 are nonlinear-enhance. The interaction factors between X3 and X10 are also nonlinear enhance. The interaction value is 0.5440 between X7 and X10. The above situation shows that the ecological landscape type, the distance from the coast, and the normalized difference vegetation index (NDVI), implemented from October 1998, influenced the landscape process. By comparing the risk mean, the following was observed. For X10, the risk mean in the nonecological landscape type is 0.73, whereas for the other types, it is 0. This indicates that the outﬂow of cultivated land is not within the spatial scope of any ecological landscape type. In X8, in an interval of (4400, 612,000), the risk mean value is more than 0.6, which implies that the arable land outﬂow is high in this range. In X6, cultivated land outﬂow focused on the elevation below 100. In X7, in an interval of (0.41, 0.656], the risk mean value is more than 0.7. The distribution of arable land ﬂow in that vegetation coverage is not very high. In X4, the higher value is concentrated in the range of (20,800, 62,000], and its value is more than 0.5, indicating that the outﬂow of arable land in the range from 20,800 m to 62,000 m is more, i.e., far away from the center of the town. (4) Inﬂow of arable land The order of the signiﬁcant Q-statistic (value > 0.1) for the landscape process of outﬂow of arable land is as follows: X10 > X6 > X7 > X8 > X5 > X9 > X11> X3 > X1. Similar to the results for the inﬂow of built-up land and outﬂow of ecological land, X10, X6, X8, X7 are the major factors. In contrast to the abovementioned landscape processes, the inﬂow of arable land has a large number of factors with strong inﬂuence. Except for X2, X4, and X12, the explanatory power of all factors exceeds 0.1. The values of X1 and X11 variables are greater than 0.1, contrary to the observations corresponding to the above processes. In this landscape process, the interactions between X1, X2, X3, and X4 are nonlinear-enhance. The interaction variables between X4 and X7, X8, X9, X11 are also nonlinear-enhance. The same is true for the interaction factors between X11 and X1, X3, X4, and between X8 and X9, X10. The maximum interaction variable value is 0.5997 between X8 and X10. By comparing the risk mean values, the following was observed. For X10, the impact on the ecological landscape type exhibits a signiﬁcant difference. The inﬂow of the arable landscape process is distributed between the woodland and grassland. In other words, the grassland and woodland have been transformed into arable land. In X8, in an interval of (2180, 4400), the risk mean is more than 0.48, which implies that the arable land inﬂow is greater in this area. In X3, X5, X6, and X7, the higher the elevation, the higher the vegetation coverage, the greater is the slope, and the farther is the distance from the coast, which leads to the larger inﬂow of arable land. In X9, in the interval (573, 12,900], the population growth is huge, but the arable land inﬂow is less. The risk mean of X1 shows that landscapes far away from the center of the development zone are easily transformed into arable land. The risk mean of X11 shows that the administrative regions that have large ﬁxed investment growth have more arable land growth, such as the Huangdao, Jimo, and Jiaozhou districts. (5) Outﬂow of salt-ﬁeld and ﬁsh farm The order of signiﬁcant Q-statistic (value > 0.1) is as follows: X7 > X3 > X6 > X9 > X8. The factor with a strong explanatory power (Q-statistic greater than 0.2) is X7. This situation reﬂects that this landscape process is inﬂuenced by the population difference, GDP in 1995, elevation, and distance from the shore. In this landscape process, there are many cases of nonlinear-enhance, indicating that the interactions of each variable are stronger in this landscape process as compared to that for the other landscape processes. The interactions between X7 and X4 are the largest, and the interaction statistic is 0.55, which is greater than the sum of X4 and X7. From the risk mean values of X7, X3, X6, X9, and X8, it is observed that the outﬂow of aquaculture landscape in the low vegetation coverage areas is more. The areas having lower elevation and, which are closer to the coast have more outﬂow of salt-ﬁeld and ﬁsh Land 2022, 11, 7 20 of 23 farm, whereas the areas having higher elevation do not have the outﬂow of salt-ﬁeld and ﬁsh farm. 4. Discussion 4.1. The Spatiotemporal Difference of Landscape Processes Affected by The Change of Land Use Pattern and Economic Development The study area is a typical bay area with high development activity and is located in Qingdao, an important coastal port city. The development of the national economy and the utilization of territorial resources in the past 30 years have greatly transformed the landscapes in the Jiaozhou Bay coastal zone. The proportion of stable and unchanged landscape was 0.716, and the changed landscape was 180,228.42 ha, accounting for 28.4%. Such a large number of landscape changes are related to the rapid development of the Jiaozhou Bay area in Qingdao. The statistical yearbook shows that the GDP of the eight administrative districts in Jiaozhou Bay (adjusted by region) increased from 13.2 billion yuan in 1990 to 104 billion yuan, and the resident population increased from 4.7 million in 1990 to 7.2 million in 2018. With the rapid economic as well as population growth, there was an urgent need for the built-up land to meet the demand. Thus, much of the landscape change was inevitable. In terms of the speed of the change, the grassland, salt-ﬁeld and ﬁsh farm, and built-up land has changed dramatically. The single dynamic degree of the three types is higher than the comprehensive dynamic attitude. Although the single dynamic degree of arable land is not high, the proportion of arable land has changed signiﬁcantly. From 1990 to 2018, on the one hand, 75,582 ha of arable land was converted to built-up land. On the other hand, 37,016.19 ha of grassland and 11,963.7 ha of built-up land were used for supplementing the arable land. The right to develop the land has always been in the hands of the government. The obligation of protecting arable land also falls on the government. The Land Administration Law of the People’s Republic of China (revision in 1998) has implemented a “requisition-compensation balance” policy of arable land [35]. Through the land-use regulation system, agricultural land is protected from excessive real estate and industrial development. Extensive rural land consolidation was carried out in 2007 [36]. The implementation of the above policy has been conﬁrmed in the present study. The amount of cultivated land in the Jiaozhou Bay region showed a balanced state from 2000 to 2010, maintained at 387,605.25 ha. Built-up land outﬂows are mostly in rural areas. In this way, the balance between cultivated land and the growth of built-up land was ensured at the expense of grassland, built-up land, wetland and waters, salt-ﬁeld breeding, and other landscape types. 4.2. Internal Relations among the Various Spatial Distribution of Landscape Processes From the spatial heterogeneity analysis of the different landscape processes, we found ﬁve landscape processes exhibiting a strong spatial autocorrelation, namely, the inﬂow of built-up land, the outﬂow of ecological land, the outﬂow of arable land, the inﬂow of arable land, and the outﬂow of salt-ﬁeld and ﬁsh farm areas. The strong spatial autocorrelation indicates that the aggregation of the landscape processes is very high. It also shows that other landscape processes are highly dispersed in space. From the distribution map of the landscape processes and the LISA cluster diagram (Figure 9), it can be seen that the inﬂow of the built-up land is mainly distributed in Jiaozhou, the Jimo administrative center, and the coast of Jiaozhou Bay, exhibiting a “high-high” distribution. Over the years, Qingdao has been adhering to the principle of “development around the bay”. It can also be seen that there is a large amount of built-up land inﬂow in the state-planned economic development zones. Arable land outﬂow and built-up land inﬂow exhibit the distribution characteristics of convergence. In China, there is a huge inﬂow of built-up land around major towns and national development zones. Another typical feature is that the expansion of urban construction land occupies the surrounding cultivated land [37]. The higher AREA_MN, CONTIG_MN, and AI index values indicate Land 2022, 11, 7 21 of 23 that the development of cultivated land and expansion of urban construction land has the characteristics of concentration and contiguity (Table 6). The landscape processes of ecological outﬂow and cultivated land inﬂow are dis- tributed in the hilly area west of Jiaozhou Bay. Combined with the huge amounts of outﬂow of ecological land in Jiaozhou Bay, it is not difﬁcult to ﬁnd that it is the source of the balance between the cultivated land occupation and compensation. The LISA cluster diagram indicates that the inﬂow of arable land and the outﬂow of ecological land has high similarity. Many grasslands have been reclaimed into arable land in the western hilly area to supplement the lost arable land. It leads to the mean center of the grassland landscape moving eastward (Figure 4). The NP, LSI, and SHAPE_MN indices show that the process of arable land inﬂow and ecological land outﬂow is relatively dispersed, and the patches are out of regular shape. The arable land reclamation is greatly affected by the topography of the hilly areas. Salt-ﬁeld and ﬁsh farm areas in northern Jiaozhou Bay were nationalized via land expropriation and were transformed into construction land. 4.3. Natural Changes, Economic and Social Development and Planning Affecting Landscape Processes It has been found that the landscape process that has a strong spatial autocorrelation often has a higher Q-statistic in the factor detector and vice versa. Therefore, ﬁve landscape processes were selected in this study for analysis, and the factors driving them were investigated. The results thus obtained show that the types of ecological landscape, the distance from cities and towns, slope, elevation, NDVI implemented from October 1998, and the GDP in 1995 are the main factors that have affected the ﬁve landscape processes. Different ecological types have a great inﬂuence on landscape processes. It can be seen that the main source of supplementary arable land is grassland. In contrast, the direct sources of construction land are not the ecological landscape, such as the forest land and grassland, but the landscape types, such as cultivated land, salt-ﬁeld and ﬁsh farms, and wetlands and waters. The outﬂow of the ecological landscape is distributed between all kinds of landscape types. With the development during the past 30 years, the ecological landscape has been damaged, and the outﬂow of ecological land is widespread. The higher elevation, slope, and vegetation coverage have led to less built-up land inﬂow. However, arable land inﬂow and ecological land outﬂow present the opposite situation. The outﬂow of ecological land was not easily affected by the distance from the coast, which is similar to the results observed for the outﬂow of arable land. Ecological land outﬂow is distributed at the outskirts of central towns, whereas arable land outﬂow is mostly distributed in the outer suburbs. 5. Conclusions By using remote sensing images, we have analyzed the Spatiotemporal heterogeneity of the landscape process. The inﬂuence of landscape processes on landscape types in the Jiaozhou Bay area over the last 30 years has been signiﬁcant, especially between 2000–2010. The inﬂow of built-up land has brought about a dramatic expansion in the amount of build-up land, by 92,311.11 ha, with most of this expansion occurring in the suburbs of the city. The expansion of built-up land comes at the expense of prime arable land around the city, accompanied by arable land outﬂow. However, the change in the amount of arable land is not signiﬁcant due to the ecological land outﬂow and built-up land outﬂow in rural areas. China’s “requisition-compensation balance” policy and rural land reclamation play a large role. The heterogeneity in the spatial distribution of landscape processes is more pro- nounced. The landscape pattern index shows a large number of patches and connectivity in arable land outﬂow and built-up land inﬂow. The outﬂow of salt-ﬁeld and ﬁsh-farm areas has the highest degree of aggregation and the largest patch size. The shape of ecological land outﬂow is most irregularly. The LISA cluster diagram revealed that, despite the spatial Land 2022, 11, 7 22 of 23 heterogeneity of landscape processes, several landscape processes had relatively similar characteristics, for example, ecological land outﬂow and arable land inﬂow, built-up land inﬂow, and arable land outﬂow. Landscape processes are affected by the elevation, slope, distance from the coast, and, especially, ecological types. The results of the OPDG model show that a gentler slope and lower elevation, and proximity to cities and town land will produce more arable land outﬂow and built-up land inﬂow. However, arable land inﬂow and ecological land outﬂow present the opposite situation. The impact of landscape processes is signiﬁcant when they involve ecological land types. This study can provide lessons for natural resource managers in the rapidly developing coastal zone area and contribute to the study of landscape processes. Land-use planning and climate change may have an impact on landscape processes. In a future study, it is necessary to explore their impact on the landscape process by a similar method. Author Contributions: Conceptualization, W.W.; methodology, all authors; software, W.W. and R.S. and Z.G.; validation, W.W.; formal analysis, W.W.; resources, Y.H.; data curation, W.W.; writing— original draft preparation, W.W.; writing—review and editing, W.W.; visualization, W.W.; supervision, Y.H.; project administration, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Natural Science Foundation of China [grant num- ber 41877034] Fundamental Research Funds for the Central Universities [grant number 2652018036] and National Marine Data and Information Service “Regulation on Differential Transformation of “Ecological-Productive-Living” Space in Coastal Zone”. Data Availability Statement: The data and R programming used to support the ﬁndings of this study are available from the authors upon request. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Déjeant-Pons, M. The European landscape convention. Landsc. Res. 2006, 31, 363–384. [CrossRef] 2. Wu, J.G. Landscape Ecology: Pattern, Process, Scale, and Hierarchy, 2rd ed.; Higher Education Press: Beijing, China, 2017; pp. 17–40. 3. Fu, B.J.; Chen, L.D.; Ma, K.M.; Wang, Y.L. Principles and Applications of Landscape Ecology, 2rd ed.; Science Press: Beijing, China, 2011; pp. 44–51. 4. 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Analysis of the Spatiotemporal Heterogeneity of Various Landscape Processes and Their Driving Factors Based on the OPGD Model for the Jiaozhou Bay Coast Zone, China

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DOI: 10.3390/land11010007
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Land – Multidisciplinary Digital Publishing Institute

Published: Dec 21, 2021

Keywords: landscape; spatiotemporal features; geographical detectors model; R “GD” package; coastal zone

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Analysis of the Spatiotemporal Heterogeneity of Various Landscape Processes and Their Driving Factors Based on the OPGD Model for the Jiaozhou Bay Coast Zone, China

Analysis of the Spatiotemporal Heterogeneity of Various Landscape Processes and Their Driving Factors Based on the OPGD Model for the Jiaozhou Bay Coast Zone, China

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Analysis of the Spatiotemporal Heterogeneity of Various Landscape Processes and Their Driving Factors Based on the OPGD Model for the Jiaozhou Bay Coast Zone, China

Analysis of the Spatiotemporal Heterogeneity of Various Landscape Processes and Their Driving Factors Based on the OPGD Model for the Jiaozhou Bay Coast Zone, China

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