A study on the evolution mechanism of agricultural landscape pattern of Dadu River Valley in Danba Region

Taking the Dadu River Basin in the Danba area of Ganzi Prefecture, Sichuan Province as the research area, based on the 2013 and 2016 Landsat8 remote sensing images, the temperature vegetation drought index (TVDI) method is used to divide the Dadu River dry valley into 6 arid gradient regions. Using ArcGIS10.5 software and Fragstats4.2 software to calculate the landscape pattern index of different arid gradient areas in different years, combined with the survey results of agricultural policies, development models, and agricultural landscape patterns in key regions, analyze the evolution of agricultural landscape patterns under different drought gradients. The results show that, except for other forestlands, the degree of landscape fragmentation is decreasing year by year on the gradient of light and moderate drought, and the degree of spatial heterogeneity is higher. On the gradient of extreme drought, the degree of landscape fragmentation is higher, and the degree of spatial heterogeneity is lower.

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Temporal and Spatial Analyses of the Landscape Pattern of Wuhan City Based on Remote Sensing Images

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7090340 ◽

2018 ◽

Vol 7 (9) ◽

pp. 340 ◽

Cited By ~ 3

Author(s):

Jianjun Lv ◽

Teng Ma ◽

Zhiwen Dong ◽

Yao Yao ◽

Zehao Yuan

Keyword(s):

Landscape Pattern ◽

Urban Areas ◽

Agricultural Landscape ◽

Urban Landscape ◽

Landscape Heterogeneity ◽

Central City ◽

National Level ◽

Landscape Patterns ◽

Wuhan City ◽

Entire Area

With the acceleration of the process of building a national-level central city in Wuhan, the landscape pattern of the city has undergone tremendous changes. In this paper, remote images are classified through the neural network classification method, based on texture extraction, and the evolution of landscape patterns was quantitatively analyzed, based on the method of moving windows, landscape metrics and urban density calculation, in order to accurately extract landscape types and perform quantitative analyses. Wuhan City is taken as an example. The surface coverage of Wuhan City from 1989 to 2016 is divided into four types: agricultural landscape clusters, forest landscape clusters, water landscape clusters, and urban landscape clusters. It was concluded that, during the study period, the landscape heterogeneity of the entire area in Wuhan has increased, but the central urban area in Wuhan has decreased. The development of urban areas has compacted inwards but expanded outwards. In addition, the western part of Wuhan City developed better than the eastern part.

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Analyzing the interpretative ability of landscape pattern to explain thermal environmental effects in the Beijing-Tianjin-Hebei urban agglomeration

PeerJ ◽

10.7717/peerj.7874 ◽

2019 ◽

Vol 7 ◽

pp. e7874

Author(s):

Dongchuan Wang ◽

Zhichao Sun ◽

Junhe Chen ◽

Xiao Wang ◽

Xian Zhang ◽

...

Keyword(s):

Surface Temperature ◽

Spatial Heterogeneity ◽

Landscape Pattern ◽

Thermal Environment ◽

Hot Spot ◽

Pearson Correlation ◽

Ordinary Least Squares ◽

Urban Agglomeration ◽

Landscape Patterns ◽

Anthropogenic Heat

The development of the urban agglomeration has caused drastic changes in landscape pattern and increased anthropogenic heat emission and lead to the urban heat island (UHI) effect more serious. Therefore, understanding the interpretation ability of landscape pattern on the thermal environment has gradually become an important focus. In the study, the spatial heterogeneity of the surface temperature was analyzed using the hot-spot analysis method which was improved by changing the calculation of space weight. Then the interpretation ability of a single landscape and a combination of landscapes to explain surface temperature was explored using the Pearson correlation coefficient and ordinary least squares regression from different spatial levels, and the spatial heterogeneity of the interpretation ability was explored using geographical weighted regression under the optimal granularity (5 × 5 km). The results showed that: (1) The hot spots of surface temperature were distributed mainly in the plains and on the southeast hills, where the landscapes primarily include artificial landscape (ArtLS) and farmland landscape (FarmLS). The cold spots were distributed mainly in the northern hills, which are dominated by forest landscape (ForLS). (2) On the whole, the interpretative ability of ForLS, FarmLS, ArtLS, green space landscape pattern, and ecological landscape pattern to explain surface temperature was stronger, whereas the interpretative ability of grassland landscape and wetland landscape to explain surface temperature was weaker. The interpretation ability of landscape pattern to explain surface temperature was obviously different in different areas. Specifically, the ability was stronger in the hills than in the plain and plateau. The results are intended to provide a scientific basis for adjusting landscape structural, optimizing landscape patterns, alleviating the UHI effect, and coordinating the balance among cities within the urban agglomeration.

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Perceptions of landscape patterns: Do the numbers count?

The Forestry Chronicle ◽

10.5558/tfc76475-3 ◽

2000 ◽

Vol 76 (3) ◽

pp. 475-480 ◽

Cited By ~ 6

Author(s):

Robert G. D'Eon ◽

Susan M. Glenn

Keyword(s):

Undergraduate Students ◽

Landscape Metrics ◽

Landscape Pattern ◽

Human Perception ◽

Landscape Fragmentation ◽

Landscape Patterns ◽

Relative Efficacy ◽

Patch Shape ◽

Before And After ◽

Patch Density

Human perception and intuition can powerfully influence how we measure and interpret landscape pattern. We compared human perception with more quantitative measures to determine their relative efficacy in arriving at conclusions about landscape pattern. We surveyed 30 professional workshop participants and 38 undergraduate students for their perception of landscape fragmentation before and after calculating a suite of landscape metrics. Participants' perception of fragmentation was most correlated with number of patches, patch density, and patch shape. Most participants retained their original intuitive response after calculating indices. We suggest that a lack of meaningful quantitative expressions for absolute landscape structure will continue to result in a dependence on intuitive human perception for management of landscape pattern. Key words: landscape pattern, forest fragmentation, perception, landscape metrics, forest management

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Landscape Pattern Changes in the Xingkai Lake Area, Northeast China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16203820 ◽

2019 ◽

Vol 16 (20) ◽

pp. 3820 ◽

Cited By ~ 2

Author(s):

Xiaohui Liu ◽

Yuan Zhang ◽

Guihua Dong ◽

Guanglei Hou ◽

Ming Jiang

Keyword(s):

Landscape Pattern ◽

Remote Sensing Data ◽

Principal Component ◽

Landscape Fragmentation ◽

Landscape Patterns ◽

Paddy Fields ◽

Lake Area ◽

Landscape Types ◽

The Difference ◽

Relative Change

Understanding landscape change is important for ecologically sustainable development. In this paper, we assessed the spatiotemporal variations of landscape pattern in the Xingkai Lake area using remote sensing data from 1982, 1995, 2000, 2005, 2010, and 2015. Landscape patterns of marshlands, paddy fields, dry farmlands, and their combinations were analyzed at class and landscape levels. We examined the stability of landscape types through principal component analysis based on class level indices for landscape types. The results indicated that marshland areas decreased significantly by 33.87% but paddy fields increased by 1.84 times from 1982 to 2015. The largest conversion of dry farmlands to paddy fields was 90.88 km2 during the period 2010–2015. In contrast, the largest conversion of paddy fields to dry farmlands was 86.03 km2 during the period 2000–2005. The difference in relative change revealed that dry farmlands had experienced a greater relative change than paddy fields since 2000. The interspersion and juxtaposition index decreased, while the number of patches grew. This showed that landscape fragmentation was increasing and the landscape pattern was becoming dispersed. Marshlands were more stable than paddy fields and dry farmlands across all time periods, except for the year 2005.

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Geographic context affects the landscape change and fragmentation caused by wind energy facilities

PeerJ ◽

10.7717/peerj.7129 ◽

2019 ◽

Vol 7 ◽

pp. e7129 ◽

Cited By ~ 4

Author(s):

Jay E. Diffendorfer ◽

Monica A. Dorning ◽

Jolene R. Keen ◽

Louisa A. Kramer ◽

Robert V. Taylor

Keyword(s):

Wind Energy ◽

Landscape Pattern ◽

Landscape Change ◽

Road Networks ◽

Landscape Fragmentation ◽

Landscape Patterns ◽

Core Area ◽

Area Index ◽

Geographic Context ◽

The Impact

Wind energy generation affects landscapes as new roads, pads, and transmission lines are constructed. Limiting the landscape change from these facilities likely minimizes impacts to biodiversity and sensitive wildlife species. We examined the effects of wind energy facilities’ geographic context on changes in landscape patterns using three metrics: portion of undeveloped land, core area index, and connectance index. We digitized 39 wind facilities and the surrounding land cover and measured landscape pattern before and after facility construction using the amount, core area, and connectivity of undeveloped land within one km around newly constructed turbines and roads. New facilities decreased the amount of undeveloped land by 1.8% while changes in metrics of landscape pattern ranged from 50 to 140%. Statistical models indicated pre-construction development was a key factor explaining the impact of new wind facilities on landscape metrics, with pre-construction road networks, turbine spacing, and topography having smaller influences. As the proportion of developed land around facilities increased, a higher proportion of the facility utilized pre-construction developed land and a lower density of new roads were built, resulting in smaller impacts to undeveloped landscapes. Building of new road networks was also a predictor of landscape fragmentation. Utilizing existing development and carefully placing turbines may provide opportunities to minimize the impacts of new wind energy facilities.

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Impacts of Landscape Patterns on Ecosystem Services Value: A Multiscale Buffer Gradient Analysis Approach

Remote Sensing ◽

10.3390/rs13132551 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2551

Author(s):

Wanxu Chen ◽

Jie Zeng ◽

Yumei Chu ◽

Jiale Liang

Keyword(s):

Sustainable Development ◽

Ecosystem Services ◽

Landscape Pattern ◽

Gradient Analysis ◽

Buffer Zone ◽

Landscape Patterns ◽

Analysis Approach ◽

Landscape Pattern Metrics ◽

Pattern Metrics ◽

Ecosystem Services Value

In recent decades, substantial changes have occurred in the spatial structure and form of landscapes in metropolises; these have greatly impacted ecosystem provision capacities. Clarifying the impact mechanism of landscape patterns on ecosystem services can provide insights into regional ecological conservation and sustainable development measures. Although previous studies have explored the impacts of landscape patterns on ecosystem services at multiple scales, few studies have been conducted using the buffer gradient analysis approach. Using land-use/cover change data, this study measured the evolution of spatiotemporal features of landscape patterns and ecosystem services value (ESV) with 1, 2, and 3 km buffer-zone scales in Wuhan, China. Econometric models were then used to analyze the impacts of landscape patterns on ecosystem services at different buffer-zone scales. The results demonstrated that rapid urbanization in Wuhan has led to significant changes in landscape patterns, and the landscape pattern metrics exhibited significant spatial heterogeneity. The ESV in Wuhan exhibited a steady decline during the study period. Hydrological regulations and waste treatment functions contributed to the largest proportion of ESV, and raw material production functions contributed to the lowest proportion. Landscape pattern metrics exerted a significant influence on ESV; however, this influence varied greatly. The results of this study provide a new understanding of the influence mechanism of landscape patterns on ecosystem services at 1, 2, and 3 km buffer-zone scales. These findings are critical for facilitating landscape planning and regional sustainable development.

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Response of Variation of Water and Sediment to Landscape Pattern in the Dapoling Watershed

Sustainability ◽

10.3390/su14020678 ◽

2022 ◽

Vol 14 (2) ◽

pp. 678

Author(s):

Chong Wei ◽

Zhiqiang Zhang ◽

Zhiguo Wang ◽

Lianhai Cao ◽

Yichang Wei ◽

...

Keyword(s):

Landscape Pattern ◽

Sediment Yield ◽

Landscape Heterogeneity ◽

Swat Model ◽

Landscape Patterns ◽

Land Resource ◽

Low Carbon ◽

Water And Sediment ◽

The Relationship ◽

Runoff And Sediment Yield

The relationship between water-sediment processes and landscape pattern changes has currently become a research hotspot in low-carbon water and land resource optimization research. The SWAT-VRR model is a distributed hydrological model which better shows the effect of land use landscape change on hydrological processes in the watershed. In this paper, the hydrological models of the Dapoling watershed were built, the runoff and sediment yield from 2006 to 2011 were simulated, and the relationship between landscape patterns and water-sediment yield was analyzed. The results show that the SWAT-VRR model is more accurate and reasonable in describing runoff and sediment yield than the SWAT model. The sub-basins whose soil erosion is relatively light are mostly concentrated in the middle reaches with a slope mainly between 0–5°. The NP, PD, ED, SPIIT, SHEI, and SHDI of the watershed increased slightly, and the COHESION, AI, CONTAG, and LPI showed a certain decrease. The landscape pattern is further fragmented, with the degree of landscape heterogeneity increasing and the connection reducing. The runoff, sediment yield and surface runoff are all extremely significantly negatively correlated with forest, which implies that for more complicated patch shapes of forest which have longer boundaries connecting with the patches of other landscape types, the water and sediment processes are regulated more effectively. Therefore, it can be more productive to carry out research on the optimization of water and soil resources under the constraint of carbon emission based on the SWAT-VRR model.

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On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

Remote Sensing ◽

10.3390/rs14020279 ◽

2022 ◽

Vol 14 (2) ◽

pp. 279

Author(s):

Qiong Wu ◽

Zhaoyi Li ◽

Changbao Yang ◽

Hongqing Li ◽

Liwei Gong ◽

...

Keyword(s):

Random Forest ◽

Linear Regression ◽

Surface Temperature ◽

Land Surface Temperature ◽

Landscape Metrics ◽

Landscape Pattern ◽

Land Surface ◽

Landscape Patterns ◽

Impervious Surfaces ◽

Multi Scale

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.

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Spatio-Temporal Variation Analysis of Landscape Pattern Response to Land Use Change from 1985 to 2015 in Xuzhou City, China

Sustainability ◽

10.3390/su10114287 ◽

2018 ◽

Vol 10 (11) ◽

pp. 4287 ◽

Cited By ~ 6

Author(s):

Yantao Xi ◽

Nguyen Thinh ◽

Cheng Li

Keyword(s):

Land Use ◽

Landscape Pattern ◽

Urban Areas ◽

Vegetation Index ◽

Agricultural Land ◽

Local Level ◽

Land Use Changes ◽

Landscape Patterns ◽

Rapid Urbanization ◽

Barren Land

Rapid urbanization has dramatically spurred economic development since the 1980s, especially in China, but has had negative impacts on natural resources since it is an irreversible process. Thus, timely monitoring and quantitative analysis of the changes in land use over time and identification of landscape pattern variation related to growth modes in different periods are essential. This study aimed to inspect spatiotemporal characteristics of landscape pattern responses to land use changes in Xuzhou, China durfing the period of 1985–2015. In this context, we propose a new spectral index, called the Normalized Difference Enhanced Urban Index (NDEUI), which combines Nighttime light from the Defense Meteorological Satellite Program/Operational Linescan System with annual maximum Enhanced Vegetation Index to reduce the detection confusion between urban areas and barren land. The NDEUI-assisted random forests algorithm was implemented to obtain the land use/land cover maps of Xuzhou in 1985, 1995, 2005, and 2015, respectively. Four different periods (1985–1995, 1995–2005, 2005–2015, and 1985–2015) were chosen for the change analysis of land use and landscape patterns. The results indicate that the urban area has increased by about 30.65%, 10.54%, 68.77%, and 143.75% during the four periods at the main expense of agricultural land, respectively. The spatial trend maps revealed that continuous transition from other land use types into urban land has occurred in a dual-core development mode throughout the urbanization process. We quantified the patch complexity, aggregation, connectivity, and diversity of the landscape, employing a number of landscape metrics to represent the changes in landscape patterns at both the class and landscape levels. The results show that with respect to the four aspects of landscape patterns, there were considerable differences among the four years, mainly owing to the increasing dominance of urbanized land. Spatiotemporal variation in landscape patterns was examined based on 900 × 900 m sub-grids. Combined with the land use changes and spatiotemporal variations in landscape patterns, urban growth mainly occurred in a leapfrog mode along both sides of the roads during the period of 1985 to 1995, and then shifted into edge-expansion mode during the period of 1995 to 2005, and the edge-expansion and leapfrog modes coexisted in the period from 2005 to 2015. The high value spatiotemporal information generated using remote sensing and geographic information system in this study could assist urban planners and policymakers to better understand urban dynamics and evaluate their spatiotemporal and environmental impacts at the local level to enable sustainable urban planning in the future.

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Does the “Returning Farmland to Forest Program” Drive Community-Level Changes in Landscape Patterns in China?

Forests ◽

10.3390/f10100933 ◽

2019 ◽

Vol 10 (10) ◽

pp. 933 ◽

Cited By ~ 2

Author(s):

Wenqing Li ◽

John Aloysius Zinda ◽

Zhiming Zhang

Keyword(s):

Regression Models ◽

Time Series Data ◽

Ecological Factors ◽

Landscape Fragmentation ◽

Community Level ◽

Landscape Patterns ◽

Series Data ◽

Farmer Livelihoods ◽

Socioeconomic Patterns ◽

Significant Factors

In China, the Returning Farmland to Forest Program (RFFP) has afforested large areas, transforming land and livelihoods. By impacting vegetation cover, it may also drive spatial pattern changes across landscapes. Most studies have focused on time series data as a means to determine the effectiveness of the program, but there is a paucity of community-level comparative studies. Twelve communities in Northwest Yunnan Province were selected to test whether the RFFP changed landscape patterns by testing the following hypotheses: with (or without) the RFFP, forest and shrubland fragmentations would decrease (or increase) and farmland fragmentation would increase (or decrease). Remote sensing images from 2000, 2010, and 2014 were used to compare the differences in landscape patterns. Survey data from 421 households were used to examine the socioeconomic and ecological factors that affect the differences in landscape fragmentation across communities. The results showed that landscape patterns and fragmentation metrics were not significantly different between communities with or without the RFFP, regardless of the class or landscape level. These communities showed consistent patterns of change in their fragmentation parameters between 2000 and 2014, with forest fragmentation decreasing and the fragmentation of farmland and the overall landscape increasing. The regression models suggest these changes were affected by the local natural conditions, socioeconomic patterns, policy implementation, and farmer livelihoods, with the proximity to market towns and elevation being significant factors. The RFFP alone did not directly drive the changes in landscape patterns for the considered region. For the new RFFP to effectively contribute to reducing fragmentation, managers of afforestation efforts should carefully consider livelihoods and biophysical factors that influence changes in landscape patterns.

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