Effects of land use classification on landscape metrics based on remote sensing and GIS

Liu, Delin; Hao, Shilong; Liu, Xianzhao; Li, Bicheng; He, Shiying; Warrington, David N.

doi:10.1007/s12665-012-1905-7

Cited by 34 publications

(18 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the one hand, it is difficult to describe the characteristics of social vulnerability if there were only few indicators, but on the other hand, if too many indicators were selected and used, there were also some problems, such as inaccessible data, complex calculation and poor operability (Cutter et al, 2003;Murphy and Scott, 2014). It is important that the selected indicators are in a manageable level, for which the PCA is a good method to use (Liu et al, 2013). For example, Cutter and Finch (2008) used the PCA method to reduce 42 social vulnerability variables to 11 independent indicators, and studied the temporal and spatial changes in social vulnerability to natural hazards using the indicators.…”

Section: Key Problems In Using Index-based Assessment Methodsmentioning

confidence: 99%

Social vulnerability of rural households to flood hazards in western mountainous regions of Henan province, China

Liu

2016

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Evaluating social vulnerability is a crucial issue in risk and disaster management. In this study, a household social vulnerability index (HSVI) to flood hazards was developed and used to assess the social vulnerability of rural households in western mountainous regions of Henan province, China. Eight key indicators were identified using existing literature and discussions with experts from multiple disciplines and local farmers, and their weights were determined using principle component analysis (PCA) and an expert scoring method. The results showed that (1) the ratio of perennial work in other places, hazard-related training and illiteracy ratio (15+) were the most dominant factors of social vulnerability. (2) The numbers of high, moderate and low vulnerability households were 14, 64 and 16, respectively, which accounted for 14.9, 68.1 and 17.0 % of the total interviewed rural households, respectively. (3) The correlation coefficient between household social vulnerability scores and casualties in a storm flood in July 2010 was significant at 0.05 significance level (r = 0.748), which indicated that the selected indicators and their weights were valid. (4) Some mitigation strategies to reduce household social vulnerability to flood hazards were proposed, which included (1) improving the local residents' income and their disaster-related knowledge and evacuation skills, (2) developing emergency plans and carrying out emergency drills and training, (3) enhancing the accuracy of disaster monitoring and warning systems and (4) establishing a specific emergency management department and comprehensive rescue systems. These results can provide useful information for rural households and local governments to prepare, mitigate and respond to flood hazards, and the corresponding strategies can help local households to reduce their social vulnerability and improve their ability to resist flood hazard.

show abstract

Section: Key Problems In Using Index-based Assessment Methodsmentioning

confidence: 99%

Social vulnerability of rural households to flood hazards in western mountainous regions of Henan province, China

Liu

2016

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…LULC analysis has become a fundamental tool in assessing the environmental consequences of human activity (Hunt and Ditzer 2001;Veldkamp and Lambin 2001;Brown 2003;Dunn 2004;Wang et al 2007;Liu et al 2011Liu et al , 2012). LULC have consequences for level of biodiversity (Tallmon et al 2003), geochemical cycles (Powers 2004) and water quality (Shippers et al 2004;Nouri et al 2009;.…”

Section: Introductionmentioning

confidence: 99%

“…Globally, numerous studies are focused on the impact of LULC on the physicochemical parameters of water (Luo et al 2003;Louisa et al 2006;Liu et al 2012;Bateni et al 2013 etc.). But here in Kashmir Himalaya, though there is a lot of information available on various limnological aspects of freshwater bodies (Pandit 1999(Pandit , 2002), yet the influences of changing catchment characteristics on the water chemistry of surface waters are not well recognized excepting a preliminary report on Nilnag Lake and its catchment land use (Qazi and Pandit 2008).…”

Section: Introductionmentioning

confidence: 99%

Influence of land use/land cover on the water chemistry of Wular Lake in Kashmir Himalaya (India)

et al. 2015

View full text Add to dashboard Cite

Introduction: The impacts of land use/land cover (LULC) pattern on surface components of water are less well recognized, particularly in the Kashmir Himalaya. The paper highlights the relationship between LULC and water chemistry for Wular Lake in Kashmir Himalaya. Methods: We used Indian Remote Sensing satellite (IRS) P6 using LISS III sensor with a spatial resolution of 23.5 m for various LULC categories. ERDAS IMAGINE 9.0 and ArcGIS 9.3 were used to generate the LULC map. Water parameters like pH and temperature were measured in the field, while parameters like dissolved oxygen, free carbon dioxide, chloride, alkalinity nitrogen and phosphorus values were determined by APHA (Standard Methods for Examination of Water and Wastewater, 1998).Results: The present study provides us the information about the proportion of different land cover categories and their correlation with some limnological parameters of Wular Lake. Among the various physicochemical parameters, low dissolved oxygen (DO) was observed in the LULC class which has the highest percentage of agricultural land (47.04%; r = 0.688 P < 0.05) followed by horticultural land (19.10%; r = 0.554; P < 0.01) in its catchment. The catchment area with greater percentage of agricultural fields drains maximum fertilizers in the lake, resulting in growth of microorganisms that deplete the dissolved oxygen content in the water body. Conclusions: Pearson matrix predicts that agricultural land, built up, horticultural land and wasteland seem to be the foremost factors responsible for the deterioration of the water quality of the lake. Careful planning of land use is needed to bring significant reductions in nutrient exports from agriculture through various tributaries, in order to restore and maintain water quality of the lake. If this trend of agriculturization manifestation continues unabated, it will be difficult to restore the lake water quality and to subsequently correct ecological manifestation of eutrophication through which the lake is undergoing at present.

show abstract

“…In this research, the selection of appropriate landscape indexes was based on experience of previous research as well as the knowledge of the study area when the spatial distribution status and data quality of research area were considered. The slight correlation landscape pattern indexes at both patch level and landscape level are selected to analyze long-term series of landscape pattern change of research area from 1986 to 2026 [25]. The number of patches (NP), patch density (PD), largest patch index (LPI), edge density (ED), mean shape index (MSI), fractal dimension index (FDI), aggregation index (AI), and interspersion juxtaposition index (IJI) were selected when landscape pattern was analyzed in patch level.…”

Section: Methodsmentioning

confidence: 99%

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

et al. 2018

View full text Add to dashboard Cite

In this work, we present a processing chain for landscape pattern and ecological security status assessment and prediction based on cellular automata Markov (CA-Markov) and pressure status response pattern (PSRP) models using remotely sensed data (RSD) captured in 1986, 1996, 2006, 2016, and RSD simulated in 2026 over Zhengzhou city, Henan province, China. Three major findings can be withdrawn through the experiments. First, there is a significant changing of landscape type area, especially for building land. The area of building land is up to more than 5%, from 1986 to 2016. Secondly, the heterogeneity of landscape is increasing, and the diversity of landscape is becoming more and more diversifying and complex. Third, the changing trend of ecological security of Zhengzhou city shaped as decreasing and increasing gradually during the last 40 years. While the ecological security status, nowadays, appeared to a good trend by contrast of the previous stages. The predicted results with CAMarkov model show that the level of ecological security is still in moderate and has a trend of moving toward to the center in 2026.

show abstract

Effects of land use classification on landscape metrics based on remote sensing and GIS

Cited by 34 publications

References 30 publications

Social vulnerability of rural households to flood hazards in western mountainous regions of Henan province, China

Social vulnerability of rural households to flood hazards in western mountainous regions of Henan province, China

Influence of land use/land cover on the water chemistry of Wular Lake in Kashmir Himalaya (India)

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

Contact Info

Product

Resources

About