Spatial pattern of non-stationarity and scale-dependent relationships between NDVI and climatic factors—A case study in Qinghai-Tibet Plateau, China

Gao, Yang; Huang, Jiao; Li, Shuang; Li, Shuangcheng

doi:10.1016/j.ecolind.2012.02.007

Cited by 82 publications

(36 citation statements)

References 19 publications

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“…GWR model is equivalent to the global regression model if the localized parameter estimates do not have statistically significant differences (Gao et al 2012b;Li et al 2010), therefore, a statistical significance test of the spatial nonstationarity was necessary. We used the corrected Akaike information criterion (AIC c ) (Hurvich and Tsai 1989;Akaike 1973), a relative measure of model performance, to compare performances of GWR and OLS models.…”

Section: Model Testmentioning

confidence: 99%

“…Several global and regional studies have revealed that NDVI can be used to indicate vegetation spatial-temporal characteristics (Moody and Johnson 2001;Piao et al 2003;Wu et al 2009;Zhang et al 2009) and has been widely employed to quantify relationships with such climatic and environmental factors as: land surface temperature (Raynolds et al 2008), precipitation (Fang et al 2001;Onema and Taigbenu 2009;Piao et al 2006), evapotranspiration (Di Bella et al 2000), and topography (Gao et al 2012a;Li et al 2006). Since global climate change became a major topic of discussion, the relations between NDVI and climatic factors have become a hot issue in geography and ecology (Gao et al 2012b;Li et al 2011;Piao et al 2006;Udelhoven et al 2009;Xiao and Moody 2004).…”

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confidence: 99%

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Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

Zhao

Gao

Wang

et al. 2014

Theor Appl Climatol

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At landscape scale, the normalized difference vegetation index (NDVI) can be used to indicate the vegetation's dynamic characteristics and has been widely employed to develop correlated and dependent relationships with the climatic and environmental factors. However, studies show that NDVI-environment relationships always emerge with complex features such as nonlinearity, scale dependency, and nonstationarity, especially in highly heterogeneous areas. In this study, we used geographically weighted regression (GWR), a local modeling technique to estimate regression models with spatially varying relationships, to investigate the spatially nonstationary relationships between NDVI and climatic factors at multiple scales in North China. The results indicate that all GWR models with appropriate bandwidth represented significant improvements of model performance over the ordinary least squares (OLS) models. The spatial relationships between NDVI and climatic factors varied significantly over space and were more significant and sensitive in the ecogeographical transition zone. Clear spatial patterns of slope parameters and local coefficient of determination (R 2 ) were found from the results of the GWR models. Moreover, the spatial patterns of the local R 2 of NDVI-precipitation are much clearer than the R 2 of NDVI-temperature in the semiarid and subhumid areas, which mean that precipitation has more significant influence on vegetation in these areas. In conclusion, the study revealed detailed site information on the variable relationships in different parts of the study area, especially in the ecogeographical transition zone, and the GWR model can improve model ability to address spatial, nonstationary, and scale-dependent problems in landscape ecology.

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Section: Model Testmentioning

confidence: 99%

mentioning

confidence: 99%

Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

Zhao

Gao

Wang

et al. 2014

Theor Appl Climatol

Self Cite

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“…The multi-scale pattern of vegetation distribution have been disclosed in numerous studies (He et al 2007;Lookingbill et al 2011;Nelson et al 2007;Propastin 2011;Gao et al 2012). In this study, four scale domains of vegetation distribution were revealed from the local wavelet spectrum of the NDVI along both latitudinal and longitudinal transects.…”

Section: The Scale-location Specific Control On Vegetation Distributionmentioning

confidence: 99%

“…In vegetation distribution related studies, GWR has been most commonly applied in recent years (Propastin 2011(Propastin , 2012Zhao et al 2010;Bayramov et al 2012;Gao et al 2012). The GWR framework has been proved to be efficient in characterizing the spatio-temporal non-stationary and scale-dependent relationships between vegetation distribution (or dynamic) patterns and their influencing factors (Propastin 2012;Gao et al 2012;Bayramov et al 2012;Propastin 2011).…”

Section: Comparison and Possible Combinations With Other Nonstationarmentioning

confidence: 99%

“…For example, vegetation cover was influenced by individual or combined effects from climate, topography, soil properties, and anthropogenic factors. The multi-scale spatial pattern of vegetation cover has been investigated in a number of recent studies (Lookingbill et al 2011;Nelson et al 2007;Propastin 2011;Gao et al 2012). Among them, the multi-scale correlation and Geographically Weighted Regression (GWR) approaches were generally applied (Nelson et al 2007;Propastin 2011).…”

Section: Introductionmentioning

confidence: 99%

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Identifying scale-location specific control on vegetation distribution in mountain-hill region

et al. 2013

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Abstract:The scale-location specific control on vegetation distribution was investigated through continuous wavelet transforms approaches in subtropical mountain-hill region, Fujian, China. The Normalized Difference Vegetation Index (NDVI) was calculated as an indicator of vegetation greenness using Chinese Environmental Disaster Reduction Satellite images along latitudinal and longitudinal transects. Four scales of variations were identified from the local wavelet spectrum of NDVI, with much stronger wavelet variances observed at larger scales. The characteristic scale of vegetation distribution within mountainous and hilly regions in Southeast China was around 20 km. Significantly strong wavelet coherency was generally examined in regions with very diverse topography, typically characterized as small mountains and hills fractured by rivers and residents. The continuous wavelet based approaches provided valuable insight on the hierarchical structure and its corresponding characteristic scales of ecosystems, which might be applied in defining proper levels in multilevel models and optimal bandwidths in Geographically Weighted Regression.

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Correlation Between Land Surface Temperature and Vegetation Cover of Nagapattinam Coastal Zone, Tamil Nadu, Using Geospatial Techniques

Rajesh

Lakshumanan

2021

Groundwater Resources Development and Planning in the Semi-Arid Region

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Spatial pattern of non-stationarity and scale-dependent relationships between NDVI and climatic factors—A case study in Qinghai-Tibet Plateau, China

Cited by 82 publications

References 19 publications

Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

Identifying scale-location specific control on vegetation distribution in mountain-hill region

Correlation Between Land Surface Temperature and Vegetation Cover of Nagapattinam Coastal Zone, Tamil Nadu, Using Geospatial Techniques

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