Dynamic Changes of Landscape Pattern in Coastal Urban Belt in Liaoning

2012 ◽  
Vol 610-613 ◽  
pp. 3765-3770
Author(s):  
Hai De Hu
Keyword(s):  
Landscape Pattern ◽  
Driving Forces ◽  
Field Investigation ◽  
Water Area ◽  
Residential Area ◽  
Remote Sensing Images ◽  
Dynamic Changes ◽  
Class Level ◽  
Salt Pan ◽  
Patch Density

Based on field investigation and social-economical data, in combination with the 1992 and 2007 Landsat TM remote sensing images of Coastal Urban Belt in Liaoning, this paper analyzed the dynamic changes of landscape pattern at both class and landscape levels and their driving forces in the study area. From 1992 to 2007, the landscape pattern in the study area experienced a significant change. At class level, the area of farmland, forestland, wetland, grassland, and abandoned land decreased, while the area of residential area, salt pan, and water area increased. At landscape level, both total number of patches and patch density increased significantly, while the largest patch index decreased, and the complication of landscape shape intensified.

The Driving Forces of Landscape Pattern in Coastal Urban Belt in Liaoning

2012 ◽  
Vol 610-613 ◽  
pp. 3771-3775
Author(s):  
Hai De Hu
Keyword(s):  
Remote Sensing ◽  
Economic Development ◽  
Population Growth ◽  
Landscape Pattern ◽  
Driving Forces ◽  
Landscape Fragmentation ◽  
Field Investigation ◽  
Rapid Population Growth ◽  
Remote Sensing Images ◽  
Growth Economic

Based on field investigation and social-economical data, in combination with the 1992 and 2007 Landsat TM remote sensing images of Coastal Urban Belt in Liaoning, this paper analyzed the driving forces of landscape pattern in the study area. From 1992 to 2007, the landscape pattern in the study area experienced a significant change. The rapid population growth, economic development and infrastructure construction had exerted strong influences on these changes of landscape pattern, and thus leading to a deeper level of landscape fragmentation

scholarly journals Landscape Pattern Change of Impervious Surfaces and Its Driving Forces in Shanghai during 1965–2010

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1956
Author(s):  
Yang Yao ◽  
Sen Zhang ◽  
Yuqing Shi ◽  
Mengqi Xu ◽  
Jiaquan Zhang ◽  
...  
Keyword(s):  
Water Quality ◽  
Landscape Pattern ◽  
Driving Forces ◽  
Surface Water Quality ◽  
Water Area ◽  
Impervious Surfaces ◽  
Rapid Urbanization ◽  
The Past ◽  
Pattern Change ◽  
Quality Variation

Rapid urbanization influences the landscape pattern of impervious surfaces, and potentially affects surface water quality. Using ArcGIS and Fragstats, this study analyzed the temporal change of the landscape pattern of impervious surfaces in Shanghai over the past 45 years, and its driving forces and impact on water quality were also analyzed. The results show that both low and high impervious surfaces showed different degrees of expansion, and as a result, the pervious surfaces and water area reduced by 40.1% and 13.8%, respectively. It proves that the fragmentation and diversity of impervious surfaces in Shanghai notably increased in the past decades, and especially the low and high impervious surfaces show substantial changes. The primary driving forces of the landscape pattern change are population density, unit area Gross Domestic Product (GDP), and the percentage of primary industry. The result of Redundancy analysis (RDA) is that the explanatory ability of landscape pattern to water quality variations decreased from 68.7% to 46.4% in the period 2000–2010. It should be stressed that the contribution of the configuration of impervious surfaces to water quality variation is less than that of the percentage of impervious surfaces.

Landscape Pattern Change and Driving Forces Analysis in Response to Urbanization

2012 ◽  
Vol 524-527 ◽  
pp. 2809-2812
Author(s):  
Chun Lin Li ◽  
Miao Liu ◽  
Yuan Man Hu
Keyword(s):  
Land Use ◽  
Landscape Pattern ◽  
Landscape Change ◽  
Green Space ◽  
Driving Forces ◽  
Principal Component ◽  
Pca Analysis ◽  
Remote Sensing Images ◽  
Pattern Change ◽  
Change Trend

In order to analysis the landscape pattern change in Shenyang, we counted the most representative landscape metrics according to the remote sensing images from 2005 to 2010. And then we used the transition matrix of land use to analyze the situation of land use macroscopically by change trend. We analyzed the driving forces of landscape change of study area from 2005 to 2010 using the multivariate statistics method of Principal component analysis (PCA). The results showed that the area of agriculture and green space decreased, and the rural settlement and urban land increased correspondingly. Based on the results of PCA analysis, the landscape changes were mainly driven by the forces of population increasing and economic development.

scholarly journals The Ecosystem Services Value Change and Its Driving Forces Responding to Spatio-Temporal Process of Landscape Pattern in the Co-Urbanized Area

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1043
Author(s):  
Xinyu Zhang ◽  
Huawei Li ◽  
Hua Xia ◽  
Guohang Tian ◽  
Yuxing Yin ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Landscape Pattern ◽  
Driving Forces ◽  
Yellow River ◽  
Landscape Patterns ◽  
Rapid Expansion ◽  
Edge Density ◽  
Cultivated Land ◽  
Construction Land ◽  
Patch Density

The development of urbanization is still expanding on the earth, and the rapid expansion of cities has changed the regional landscape pattern and significantly affected the value of regional ecosystem services in developing countries such as China. Zhongmu County, as the core area of Zhengzhou-Kaifeng integration, studying the temporal and spatial transformations of its landscape pattern and ecosystem service value (ESV) is of great significance to the region’s sustainable development. Based on remote sensing images and socioeconomic data, this study aims to explore the landscape pattern of Zhongmu County from 2005 to 2018 and its impact on ESV. Research methods include an ESV equivalent factor method, landscape pattern index, spatial autocorrelation, and other methods. The results showed that: (1) During the study period, the patch density and shape complexity, landscape diversity, and fragmentation of the overall landscape in the study area continued to increase while landscape connectivity decreased. (2) The total amount of ESV increased by 10.05 million USD; ESV had certain differences in spatial distribution: high-value clusters were mainly located at the boundary of the Yellow River in the north, while low-value clusters had a significant eastward expansion trend. (3) ESV increased significantly in areas where cultivated land was transferred to waters and forests, and ESV in areas where waters transferred to construction land and cultivated land was significantly reduced. (4) ESV had a significant positive spatial correlation with patch density, edge density, mean patch fractal dimension, mean patch size, and the landscape shape index, and a spatially negative correlation with the contagion index and cohesion index. The spatial and temporal changes in landscape patterns and ESV were all mainly driven by the regional development “Zhengzhou-Kaifeng integration” policy. Therefore, the ESV can be improved, and the ecological security of the urban integration area can be guaranteed through policy measures such as optimizing the layout of construction land and adjusting the uniform distribution of green areas by the land-use policy.

A Study of Ecological Security Assessment for Natural Reserve

2015 ◽  
Vol 1092-1093 ◽  
pp. 1081-1086
Author(s):  
Zhao Yan Diao ◽  
De Rong Su ◽  
Shi Hai Lv ◽  
Zhi Rong Zheng ◽  
Sheng Xing Ye ◽  
...  
Keyword(s):  
Landscape Pattern ◽  
Spatial Information ◽  
Ecological Security ◽  
Security Assessment ◽  
Ecological Safety ◽  
Topographic Map ◽  
Safety Index ◽  
Dynamic Changes ◽  
Natural Reserve ◽  
Safe Area

Based on TM remote sensing image and topographic map, the spatial information of landscape pattern was extracted in study areas in 1990, 2000 and 2010. With the principles and methods of landscape ecology, land use/cover change, ecosystem service values were selected to construct the ecological safety index which was used to quantitatively analyze the dynamic changes of landscape pattern and elucidated ecological safety status in study area. Result showed that During the 20 years from 1990 to 2010, as human factors interference increase, the farmland and construction land increased by 21.11% and 15.38% respectively, the grassland area is reduced by 27.57%.Cropland had an increased trend during the period of 1990 to 2010 period, The wood land and swamp land had an increased trend during the period of 1990 to 2000 area also, but reduced during from 2000 to 2010.From 1990 to 2010, the whole study area lied in the level of relatively safer, but the safe area was reduced the amount of 4967 ha during 2000 to 2010. The relatively safer land areas was increased first then reduced during the whole study period, the relatively non-safer level land and relatively safer level land areas were accounting for 10.81% and 2.92% of the total area respectively.

Study on Dynamic Changes of Soil Erosion in the North and South Mountains of Lanzhou

2021 ◽  
Author(s):  
Hua Zhang ◽  
Jinping Lei ◽  
Cungang Xu ◽  
Yuxin Yin
Keyword(s):  
Soil Erosion ◽  
Environmental Factors ◽  
Water Area ◽  
Vegetation Coverage ◽  
Dynamic Changes ◽  
The North ◽  
Joint Influence ◽  
Bare Land ◽  
Temporal And Spatial ◽  
North And South

Abstract This study takes the north and south mountains of Lanzhou as the study area, calculates the soil erosion modulus of the north and south mountains of Lanzhou based on the five major soil erosion factors in the RUSLE model and analyzes the temporal and spatial dynamic changes of soil erosion and the characteristics of soil erosion under different environmental factors. The results show that the soil erosion intensity of the north and south mountains of Lanzhou is mainly micro erosion in 1995, 2000, 2005, 2010, 2015 and 2018. They are distributed in the northwest and southeast of the north and south mountains. Under different environmental factors, the soil erosion modulus first increased and then decreased with the increase of altitude; the soil erosion modulus increased with the increase of slope; the average soil erosion modulus of grassland and woodland was larger, and the average soil erosion modulus of water area was the smallest; except for bare land, the average soil erosion modulus decreased with the increase of vegetation coverage. The soil erosion modulus in the greening range is lower than that outside the greening scope, mainly the result of the joint influence of precipitation, soil and vegetation.

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