Landscape pattern changes at a catchment scale: a case study in the upper Jinjing river catchment in subtropical central China from 1933 to 2005

Liu, Xinliang; Li, Yong; Shen, Jianlin; Fu, Xiaoqing; Xiao, Runlin; Wu, Jinshui

doi:10.1007/s11355-013-0221-z

Cited by 17 publications

(8 citation statements)

References 51 publications

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“…However, the most important factor was DEM in the entire catchment and Subcatchments 1 & 39. This was mainly due to the characteristics of little plains interbedded with hilly in the Jinjing river catchment [29]. The less important factors were different for woodland and paddy field in Subcatchment 1.…”

Section: Selection Of Driving Forces With Spatial Scalesmentioning

confidence: 97%

Comparing Eight Computing Algorithms and Four Consensus Methods to Analyze Relationship between Land Use Pattern and Driving Forces

Liu¹,

Wang²,

Li³

et al. 2019

IJG

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Although many computing algorithms have been developed to analyze the relationship between land use pattern and driving forces (RLPDF), little has been done to assess and reduce the uncertainty of predictions. In this study, we investigated RLPDF based on 1990, 2005 and 2012 datasets at two spatial scales using eight state-of-the-art single computing algorithms and four consensus methods in Jinjing rive catchment in Hunan Province, China. At the entire catchment scale, the mean AUC values were between 0.715 (ANN) and 0.948 (RF) for the single-algorithms, and from 0.764 to 0.962 for the consensus methods. At the subcatchment scale, the mean AUC values between 0.624 (CTA) and 0.972 (RF) for the single-algorithms, and from 0.758 to 0.979 for the consensus methods. At the subcatchment scale, the mean AUC values were between 0.624 (CTA) and 0.972 (RF) for the single-algorithms, and from 0.758 to 0.979 for the consensus methods. The result suggested that among the eight single computing algorithms, RF performed the best overall for woodland and paddy field; consensus method showed higher predictive performance for woodland and paddy field models than the single computing algorithms. We compared the simulation results of the bestand worst-performing algorithms for the entire catchment in 2012, and found that approximately 72.5% of woodland and 72.4% of paddy field had probabilities of occurrence of less than 0.1, and 3.6% of woodland and 14.5% of paddy field had probabilities of occurrence of more than 0.5. In other words, the simulation errors associated with using different computing algorithms can be up to 14.5% if a probability level of 0.5 is set as the threshold. The re-How to cite this paper: Liu, X.sults of this study showed that the choice of modeling approaches can greatly affect the accuracy of RLPDF prediction. The computing algorithms for specific RLPDF tasks in specific regions have to be localized and optimized.

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Section: Selection Of Driving Forces With Spatial Scalesmentioning

confidence: 97%

Comparing Eight Computing Algorithms and Four Consensus Methods to Analyze Relationship between Land Use Pattern and Driving Forces

Liu¹,

Wang²,

Li³

et al. 2019

IJG

Self Cite

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“…For examples, paddy fields were transformed to tea fields and vegetable fields surrounding the three urban core areas in order to promote economic development ( Figure 5), and several tea manufacturing companies and vegetable-production bases have been established in recent decades. Specifically, from 1990 to 2012, 102 ha of paddy fields were converted to tea fields and vegetable fields in Jinjing Town [38].…”

Section: Urban Core Sprawl and Residential Migrationmentioning

confidence: 99%

Quantifying the Spatio-Temporal Process of Township Urbanization: A Large-Scale Data-Driven Approach

Liu¹,

Wang²,

Li³

et al. 2019

IJGI

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The integrated recognition of spatio-temporal characteristics (e.g., speed, interaction with surrounding areas, and driving forces) of urbanization facilitates regional comprehensive development. In this study, a large-scale data-driven approach was formed for exploring the township urbanization process. The approach integrated logistic models to quantify urbanization speed, partial triadic analysis to reveal dynamic relationships between rural population migration and urbanization, and random forest analysis to identify the response of urbanization to spatial driving forces. A typical subtropical town was chosen to verify the approach by quantifying the spatio-temporal process of township urbanization from 1933 to 2012. The results showed that (i) urbanization speed was well reflected by the changes of time-course areas of urban cores fitted by a four-parameter logistic equation (R2 = 0.95–1.00, p < 0.001), and the relatively fast and steady developing periods were also successfully predicted, respectively; (ii) the spatio-temporal sprawl of urban cores and their interactions with the surrounding rural residential areas were well revealed and implied that the town experienced different historically aggregating and splitting trajectories; and (iii) the key drivers (township merger, elevation and distance to roads, as well as population migration) were identified in the spatial sprawl of urban cores. Our findings proved that a comprehensive approach is powerful for quantifying the spatio-temporal characteristics of the urbanization process at the township level and emphasized the importance of applying long-term historical data when researching the urbanization process.

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“…Study on landscape spatial pattern can provide significant information for rational management to the environmental resources, and research on landscape pattern change is a hotspot of landscape ecology at present (Xiang et al, 2016). Landscape indices are effective in studying landscape pattern (Liu et al, 2014;Fan et al, 2016). Through a single or multiple index in combination, linking pattern and process, and making the ecological concrete numerical value through its characterization to describe regional landscape pattern and its change.…”

Section: Landscape Pattern Analysismentioning

confidence: 99%

Ecosystem Health Assessment of Mining Cities Based On Landscape Pattern

Liu

Lin

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Ecosystem health assessment (EHA) is one of the most important aspects in ecosystem management. Nowadays, ecological environment of mining cities is facing various problems. In this study, through ecosystem health theory and remote sensing images in 2005, 2009 and 2013, landscape pattern analysis and Vigor-Organization-Resilience (VOR) model were applied to set up an evaluation index system of ecosystem health of mining city to assess the healthy level of ecosystem in Panji District Huainan city. Results showed a temporal stable but high spatial heterogeneity landscape pattern during [2005][2006][2007][2008][2009][2010][2011][2012][2013]. According to the regional ecosystem health index, it experienced a rapid decline after a slight increase, and finally it maintained at an ordinary level. Among these areas, a significant distinction was presented in different towns. It indicates that the ecosystem health of Tianjijiedao town, the regional administrative centre, descended rapidly during the study period, and turned into the worst level in the study area. While the Hetuan Town, located in the northwestern suburb area of Panji District, stayed on a relatively better level than other towns. The impacts of coal mining collapse area, land reclamation on the landscape pattern and ecosystem health status of mining cities were also discussed. As a result of underground coal mining, land subsidence has become an inevitable problem in the study area. In addition, the coal mining subsidence area has brought about the destruction of the farmland, construction land and water bodies, which causing the change of the regional landscape pattern and making the evaluation of ecosystem health in mining area more difficult. Therefore, this study provided an ecosystem health approach for relevant departments to make scientific decisions.

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Landscape pattern changes at a catchment scale: a case study in the upper Jinjing river catchment in subtropical central China from 1933 to 2005

Cited by 17 publications

References 51 publications

Comparing Eight Computing Algorithms and Four Consensus Methods to Analyze Relationship between Land Use Pattern and Driving Forces

Comparing Eight Computing Algorithms and Four Consensus Methods to Analyze Relationship between Land Use Pattern and Driving Forces

Quantifying the Spatio-Temporal Process of Township Urbanization: A Large-Scale Data-Driven Approach

Ecosystem Health Assessment of Mining Cities Based On Landscape Pattern

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