Landscape pattern evolution and driving factors of Songhua River wetland in Harbin

Simulation on evolution of landscape pattern is a hot problem because the evolution of terrestrial landscape pattern will be related directly to the changes of climate. In the study, it presents a neural network model for the evolution of landscape pattern by using the landscape pattern transformation rules and parameters. Owing to the typical vertical zoning of vegetation, Mount ChangBai is taken as an example to demonstrate the application of simulation model. Landsat TM data in 1985 and 1999 are combined with the geographic data. The landscape pattern evolution parameters are built and the transformation rules are confirmed by the help of the three layers Back Propagation (BP) Neural Network. There are 16 neural cells for the input layer and 11 cells for the output cell. The evolution of the landscape pattern in 2013 and 2027 are predicted by the model. The precision of the model in 1985 was 84% by taking the year of 1999 as starting point, while the precision of the model in 1999 was 82% by taking the year of 1985 as starting point. The simulation result was very close to actual situation by comparison with Moran I index in 1985 and 1999.

The rapid expansion and development of the city have introduced a large number of surrounding rural populations into the city, resulting in the continuous outward expansion of the city area. The expansion of the town leads to changes in the morphology and number of patches in the urban fringe area, and a larger degree of expansion leads to loosen urban morphology, landscape fragmentation, and a reduction in the efficiency of land use, resulting in a series of problems such as loss of arable land, waste of resources, and damage to the ecological environment. Landscape pattern is an expression of land use, and a series of problems brought about by urban expansion will directly cause the evolution of landscape patterns. This paper introduces the research methods and main techniques of urban edge expansion based on remote sensing techniques and analyses the impact of urban edge expansion on the evolution of landscape patterns. Results show that studying urban edge expansion based on remote sensing technology includes buffer analysis using remote sensing data, establishing landscape indicators, and then discussing their impacts on the evolution of landscape patterns. The buffer zone analysis can identify the buffer zones of different circles to determine the scope of urban expansion in various periods, while the landscape pattern index analysis can evaluate the evolution of landscape patterns after the calculation and statistics of data. Finally, the impact of urban expansion on landscape pattern changes is mainly reflected in the fragmentation, heterogeneity, and complexity of the landscape.

In order to overcome the problem that the urban green landscape pattern construction effect is not ideal, this paper proposes the research method of spatio-temporal evolution of urban complex landscape pattern based on remote sensing technology. This method collects landscape pattern information. Landscape images are processed by radiometric correction and geometric correction, and spatial pattern indices are calculated. The experimental results show that: under the research method of spatiotemporal evolution of landscape pattern in this paper, the landscape area of dry land is reduced more, the transfer volume of residential, industrial and mining land and water area is less, and the stability is stronger. The density of landscape edge increased, indicating that the degree of landscape fragmentation in the city has a deepening trend. In terms of landscape spatial heterogeneity, the overall pattern tends to be coordinated, which indicates that the proposed method has good application performance.

黄土丘陵沟壑区景观格局演变特征——以陕西省延安市为例

Guiding the landscape patterns evolution is the key to mitigating river water quality degradation

With the support of RS, GIS and FRAGSTATS techniques, the landscape pattern has been analyzed quantitatively in Dianchi basin by 16 commonly used landscape metrics, based on remote sensing images of 1988, 1990, 1994, 1999, 2002 and 2008. After performance of principal component analysis (PCA) on the 16 landscape metrics, three principal components (PCs) were generalized: spatial aggregation of landscape patches, landscape fragmentation and landscape diversity. Then, the characteristics and evolution of landscape pattern in Dianchi basin have been explored at the landscape level. The results showed, that, from 1988 to 1994, the landscape fragmentation was serious and the level of diversity was fluctuant. And for some kinds of landscape patches, the integrity was much undermined, the spatial distribution was scattered and the degree of aggregation was fallen. From 1994 to 2008, the degree of aggregation between patches increased gradually. The situation of landscape fragmentation was under control. And, the land use types had a tendency towards diversification and homogenization.

Land use transitions cause reconfigurations of regional landscape patterns which can further change the regional ecosystem service functions and its values, especially in environmentally fragile regions. Firstly, this paper theoretically examines the relationships between land use transitions, landscape pattern evolution and the responses of ecosystem service functions in the Guangxi Zhuang Autonomous Region (Guangxi). Then, it explores the spatio-temporal evolution features of land use transition by using land use change matrices, examines landscape patterns by using the landscape pattern index, and studies ecosystem service value (ESV) by revising the coefficients of ESV per unit area. Finally, focus is placed on the empirical analysis of ESV responses to landscape pattern evolution caused by land use transitions in Guangxi. The results show that: (1) Guangxi has undergone an overall intensity-changing process of land use transition at a moderate rate during 1990–2010 and at a drastic rate during 2010–2018. In general, the area of construction land and waterbodies has increased, while forested land, grassland and farmland have decreased. Landscape fragmentation and heterogeneity are higher in the central area than that in the surrounding areas, while patch aggregation and connectivity show an opposite trend. Forested land patches are highly clustered, while grassland and farmland are fragmented and scattered and construction land patches tend to have aggregated. (2) The total loss of ESV has reached 20.56 billion RMB in Guangxi, and all areas’ single ESVs have decreased to different degrees during the past 28 years. Spatially, the ESV distribution shows a differentiated pattern of low in the central plain and high in the surrounding mountain regions which are mainly dominated by high-value zones. (3) The total ESV has significant positive correlations with the largest patch index (LPI), COHESION and the Aggregation Index (AI), and significant negative correlations with the Number of Patches (NP) and the Shannon Diversity Index (SHDI), while the correlation with the Landscape Shape Index (LSI) is not significant, indicating that the influence on ESV caused by landscape pattern evolution varies greatly. (4) The change of land area and multi-directional shifts among different land use types caused by land use transitions in Guangxi could both lead to the evolution of landscape patterns. Further, ecological service function responded obviously to the landscape pattern evolution in Guangxi, causing significant changes in strengthening or weakening of the ecological service function and its value. This systematic analysis should help coordinate the relationship of regional land use regulation, landscape pattern optimization and ecosystem operation in Guangxi or even China.

The temporal and spatial evolution of landscape pattern is the most intuitive form of land use transition. Analyzing the change of landscape pattern and its driving factors is of great significance to land use management and water quality protection in the basin. Based on the land use data obtained from the remote sensing image interpretation of the Yellow River Basin (Henan section) in 1990, 2000, 2010, and 2020, the landscape pattern evolution characteristics of the Yellow River Basin (Henan section) were quantitatively studied using the methods of multi-angle land use transfer matrix, land use information atlas, and landscape pattern index, and the influencing factors of landscape pattern evolution of the Yellow River Basin (Henan section) were revealed using the geographic detectors (a new statistical method to measure the explanatory power of independent variables to dependent variables mainly by analyzing the overall differences among various types of geographical spaces). The results show that: (1) From 1990 to 2020, the mutual transformation of land use types in the Yellow River Basin (Henan section) was frequent, and the transformation tracks were diversified. Among them, the outflow behavior of land use types is mainly manifested in the transformation from cultivated land to construction land, and the inflow behavior of land use types is mainly manifested in the transformation from grassland and water to cultivated land. (2) In the information map of land use change in the Yellow River Basin (Henan section) from 1990 to 2020, the stable type had the widest distribution range, accounting for 94.60% of the total area of the study area, with two main change patterns: “cultivated land-cultivated land-cultivated land-cultivated land” and “woodland-woodland-woodland-woodland”, which indicates that the landscape pattern of the basin dominated by cultivated land and woodland has not changed fundamentally. The four land use change structure types, repeated change, early change, intermediate change and continuous change, account for a relatively small proportion and are concentrated in the vicinity of the Yellow River. (3) At the landscape level, the watershed generally shows the trend of decreasing landscape fragmentation, increasing landscape heterogeneity and constantly balancing landscape patch types. At the level of patch type, the landscape dominance of cultivated land decreases, while that of construction land increases. The occupation of construction land is the main reason for the fragmentation and homogenization of cultivated land. (4) From the perspective of landscape scale and patch type scale, through the geographical exploration of various natural factors and socio-economic factors that potentially affect the landscape pattern evolution, it is found that the spatial differences of natural factors such as slope, elevation, temperature, and precipitation can better reflect the spatial heterogeneity of the landscape pattern in the Yellow River Basin (Henan section) than those of socio-economic factors such as GDP and population density, and the interaction of any two driving factors has a greater influence on the spatial distribution characteristics of landscape pattern than any single factor, indicating that the formation of spatial heterogeneity in the Yellow River Basin (Henan section) is the result of the interaction of various influencing factors. The results of this study can provide ideas for exploring the trend and influencing mechanism of landscape pattern change in the basin, and have important reference significance for ecological environment management, ecosystem protection, and land use planning in the Yellow River Basin (Henan section).

This paper investigates the impact of land use/cover type changes in the Haideigou open-pit coal mine on the evolution of the landscape patterns and ecological and environmental quality in the mine area, based on medium- and high-resolution remote sensing images in 2006, 2011, 2016, and 2021 using ArcGIS 10.5, Fragstats 4.2, and the Google Earth Engine platform. The results show that: (1) From 2006 to 2021, the area of cropland and waste dumps in the Heidaigou mining area changed significantly, the land use shifted in a single direction, and the overall land use change was unbalanced. (2) Through the analysis of landscape indicators, it was shown that the diversity of the landscape patches in the study area increased, connectivity decreased, and the patches became more fragmented. (3) Based on the changes in the mean value of the RSEI over the past 15 years, the ecological environment quality of the mining area deteriorated first and then improved. The quality of the ecological environment in the mining area was significantly affected by human activities. This study provides an important basis for achieving the sustainability and stability of ecological environmental development in mining areas.

Regional proximity effects of landscape pattern evolution: Evidence from 325 county-level areas in the middle reaches of the Yangtze River, China

Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization

旅游干扰下流域多尺度景观格局演化特征及驱动因素——以新安江流域为例

Research on the spatiotemporal evolution of land use landscape pattern in a county area based on CA-Markov model

The change of urban landscape caused by human activities is one of the most important factors affecting terrestrial ecosystem. The distribution of urban landscape pattern has great impacts on the service function of regional biodiversity. To reveal the variation of landscape pattern and habi-tat quality in cities and its driving factors, we extracted landscape type information of Wuhan in 2005, 2010, 2015, and analyzed spatial-temporal evolution of landscape pattern using Markov transition model. The CA-Markov model was used to simulate the landscape pattern in 2020 under the natural growth scenario. The driving factor for landscape variation was analyzed using Logistic regression model. Combined with InVEST model, spatial pattern of habitat quality and its variation in three phases were calculated and evaluated. The simulated habitat quality in 2020 was obtained and its distribution characteristics were analyzed. The relationship between variation of landscape pattern and human activities was explored. The results showed that cultivated land and manufactured surface were the landscape types with highest variations between 2005 and 2015. The area of cultivated land continued to decline, with most of the area being transferred into manufactured surface. The area of manufactured surface continued to increase, most of which was transferred from paddy field and dry land. From 2005 to 2015, the habitat quality declined, with a large number of landscapes with high habitat quality level being changed to low habitat quality level. The overall index of habitat quality decreased and the biodiversity service function declined, indicating the degeneration of habitat quality. In 2015-2020, the evolutionary trend of landscape pattern and habitat quality would keep consistent with the past decade, with an increasing area of artificial surface, decreasing index of habitat quality, weakening biodiversity service function, and degenerating habitat quality. The most important factor accounted for the landscape pattern change in the study area was the changes in Gross Domestic Product (GPD) and regional fiscal revenue. Human socio-economic activities were the key driving force for the spatial variation of landscape and degeneration of habitat quality. Urbanization and land reclamation by filling lakes were the main reasons for landscape pattern variation in Wuhan.

The spatial pattern of landscape has great influence on the biodiversity provided by ecosystem. Understanding the impact of landscape pattern dynamics on habitat quality is significant in regional biodiversity conservation, ensuring ecological security guarantee, and maintaining the ecological environmental sustainability. Here, combining CA-Markov and InVEST model, we investigated the evolution of landscape pattern and habitat quality, and presented an explanation for variability of biodiversity linked to landscape pattern in Hubei section of Three Gorges Reservoir Area (TGRA). The spatial-temporal evolution characteristic of landscape pattern from 1990 to 2010 were analyzed by Markov chain. Then, the spatial pattern of habitat quality and its variation in three phases were computed by InVEST model. The driving force for landscape variation was explored by using Logistic regression analysis. Next, the CA-Markov model was used to simulate the future landscape pattern in 2020. Finally, future habitat quality maps were obtained by InVEST model predicted landscape maps. The results concluded that, the overall landscape pattern has changed slightly from 1990 to 2010. Woodland, waters and construction land had the greatest variations in proportion among the landscape types. The area of woodland has been decreasing gradually below the average elevation of 140 m, and the area of waters and construction land increased sharply. Logistics regression results indicated that terrain and climate were the most influencing natural factors compared with human factors. The Kappa coefficient reached 0.92, indicating that CA-Markov model had a good performance in future landscape prediction by adding nighttime light data as restriction factor. The biodiversity has been declining over the past 20 years due to the habitat degradation and landscape pattern variation. Overall, the maximum values of habitat degradation index were 0.1188, 0.1194 and 0.1195 respectively, showing a continuously increasing trend from 1990 to 2010. Main urban areas of Yichang city and its surrounding areas has higher habitat degradation index. The average values of habitat quality index of the whole region were 0.8563, 0.8529 and 0.8515 respectively, showing a continuously decreasing trend. The lower habitat quality index mainly located in the urban land as well as the main and tributary banks of the Yangtze River. Under the business as usual scenario, habitat quality continued to maintain the variation trend of the previous decade, showing a reducing habitat quality index and an increasing area of artificial surface. Under the ecological protection scenario, the variation of habitat quality in this scenario represented reverse trend to the previous decade, exhibiting an increase of habitat quality index and an increasing area of woodland and grassland. Construction of Three Gorges Dam, impoundment of Three Gorges Reservoir (TGR), resettlement of Three Gorges Project and urbanization were the most explanatory driving forces for landscape variation and degradation of habitat quality. The research may be useful for understanding the impact of landscape pattern dynamics on biodiversity, and provide scientific basis for optimizing regional natural environment, as well as effective decision-making support to local government for landscape planning and biodiversity conservation.