Forest landscape patterns dynamics of Yihe-Luohe river basin

2003 ◽  
Vol 13 (2) ◽  
pp. 153-162
Author(s):  
Ding Shengyan ◽  
Shang Fude ◽  
Qian Lexiang ◽  
Cao Xinxiang ◽  
Li Shuang ◽  
...  
Keyword(s):  
River Basin ◽  
Landscape Patterns ◽  
Forest Landscape

scholarly journals Extracting Frequent Sequential Patterns of Forest Landscape Dynamics in Fenhe River Basin, Northern China, from Landsat Time Series to Evaluate Landscape Stability

2021 ◽  
Vol 13 (19) ◽  
pp. 3963
Author(s):  
Yue Zhang ◽  
Xiangnan Liu ◽  
Qin Yang ◽  
Zhaolun Liu ◽  
Yu Li
Keyword(s):  
Time Series ◽  
River Basin ◽  
Landscape Pattern ◽  
Pattern Mining ◽  
Northern China ◽  
Shape Index ◽  
Landscape Patterns ◽  
Forest Landscape ◽  
Distribution Law ◽  
Scientific Support

The forest landscape pattern evolution can reveal the intensity and mode of action of human–land relationships at different times and in different spaces, providing scientific support for regional ecological security, human settlement health, and sustainable development. In this study, we proposed a novel method for analyzing the dynamics of landscape patterns. First, patch density (PD), largest patch index (LPI), landscape shape index (LSI), and contiguity index (CI) were used to identify the types of forest spatial patterns. The frequent sequential pattern mining method was used to detect the frequent subsequences from the time series of landscape pattern types from 1991 to 2020 and further evaluate the forest landscape stability of the Fenhe River Basin in China. The results show that different frequent sequence patterns have conspicuous spatial and temporal differences, which describe the evolution processes and stability changes during a certain period of forest evolution and play an important role in the analysis of forest dynamics. The proportion of the disturbed regions to the total forest area exhibited a downward trend. The long-term evolution pattern indicates that there are many evolution processes and trends in the forest at the same time, showing an aggregation distribution law. Compared with 2016, the forest landscape has become complete in 2020, and the overall stability of the Fenhe River Basin has improved. This study can provide scientific support to land managers and policy implementers and offer a new perspective for studying forest landscape pattern changes and evaluating landscape stability.

scholarly journals Modeling Land Use Changes and their Impacts on Non-Point Source Pollution in a Southeast China Coastal Watershed

2018 ◽  
Vol 15 (8) ◽  
pp. 1593 ◽  
Author(s):  
Xin Zhang ◽  
Lin Zhou ◽  
Yuqi Liu
Keyword(s):  
Land Use ◽  
Point Source ◽  
River Basin ◽  
Landscape Metrics ◽  
Land Use Changes ◽  
Landscape Patterns ◽  
Cultivated Land ◽  
Point Source Pollution ◽  
Coastal Watershed ◽  
Non Point Source Pollution

Changes in landscape patterns in a river basin play a crucial role in the change on load of non-point source pollution. The spatial distribution of various land use types affects the transmission of non-point source pollutants on the basis of source-sink theory in landscape ecology. Jiulong River basin in southeast of China was selected as the study area in this paper. Aiming to analyze the correlation between changing landscape patterns and load of non-point source pollution in this area, traditional landscape metrics and the improved location-weighted landscape contrast index based on the minimum hydrological response unit (HRULCI) were applied in this study, in combination with remote sensing and geographic information system (GIS) technique. The results of the landscape metrics showed the enhanced fragmentation extent and the decreasing polymerization degree of the overall landscape in the watershed. High values of HRULCI were concentrated in cultivated land, while low HRULCI values mostly appeared in forestland, indicating that cultivated land substantially enhanced non-point source pollution, while forestland inhibited the pollution process.

scholarly journals Measuring forest landscape patterns in the cascade range of Oregon, USA

1991 ◽  
Vol 57 (1) ◽  
pp. 73-88 ◽  
Author(s):  
William J. Ripple ◽  
G.A. Bradshaw ◽  
Thomas A. Spies
Keyword(s):  
Cascade Range ◽  
Landscape Patterns ◽  
Forest Landscape

scholarly journals A New Indicator to Better Represent the Impact of Landscape Pattern Change on Basin Soil Erosion and Sediment Yield in the Upper Reach of Ganjiang, China

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 990
Author(s):  
Yongfen Zhang ◽  
Nong Wang ◽  
Chongjun Tang ◽  
Shiqiang Zhang ◽  
Yuejun Song ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
River Basin ◽  
Landscape Metrics ◽  
Landscape Pattern ◽  
Sediment Yield ◽  
River Basins ◽  
Landscape Patterns ◽  
Soil Erodibility ◽  
Land Use Pattern

Landscape patterns are a result of the combined action of natural and social factors. Quantifying the relationships between landscape pattern changes, soil erosion, and sediment yield in river basins can provide regulators with a foundation for decision-making. Many studies have investigated how land-use changes and the resulting landscape patterns affect soil erosion in river basins. However, studies examining the effects of terrain, rainfall, soil erodibility, and vegetation cover factors on soil erosion and sediment yield from a landscape pattern perspective remain limited. In this paper, the upper Ganjiang Basin was used as the study area, and the amount of soil erosion and the amount of sediment yield in this basin were first simulated using a hydrological model. The simulated values were then validated. On this basis, new landscape metrics were established through the addition of factors from the revised universal soil loss equation to the land-use pattern. Five combinations of landscape metrics were chosen, and the interactions between the landscape metrics in each combination and their effects on soil erosion and sediment yield in the river basin were examined. The results showed that there were highly similar correlations between the area metrics, between the fragmentation metrics, between the spatial structure metrics, and between the evenness metrics across all the combinations, while the correlations between the shape metrics in Combination 1 (only land use in each year) differed notably from those in the other combinations. The new landscape indicator established based on Combination 4, which integrated the land-use pattern and the terrain, soil erodibility, and rainfall erosivity factors, were the most significantly correlated with the soil erosion and sediment yield of the river basin. Finally, partial least-squares regression models for the soil erosion and sediment yield of the river basin were established based on the five landscape metrics with the highest variable importance in projection scores selected from Combination 4. The results of this study provide a simple approach for quantitatively assessing soil erosion in other river basins for which detailed observation data are lacking.

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