Mapping soil organic matter with limited sample data using geographically weighted regression

Wang, Ke; Zhang, C.R.; Li, W.D.; Lin, Jie; Zhang, D.X.

doi:10.1080/14498596.2013.812024

Cited by 19 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This means that the global OLS model can capture only 41 percent of the influence of the explanatory variables, but the GWR method represents a significant improvement. In general, above results prove that the GWR model performed much better than the OLS model did, which is consistent with findings in previous studies (Jaimes, Sendra, Delgado, & Plata, 2010;Oliveira, Pereira, San-Miguel-Ayanz, & Lourenço, 2014;Qu, Li, Zhang, Huang, & Zhao, 2014;Wang, Zhang, Li, Lin, & Zhang, 2014).…”

Section: Gwr Resultssupporting

confidence: 82%

Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China

Yang

et al. 2016

Applied Geography

122

View full text Add to dashboard Cite

a b s t r a c tLand price plays an important role in guiding land resource allocation for urban planning and development, particularly in big cities of fast developing countries where infrastructures and populations change frequently. Therefore, detecting spatially implicit information in the spatial pattern of relationships between land price and related impact factors is critical. Geographically weighted regression (GWR) analysis was conducted in this study for the purpose in Wuhan, China, by using a 10-year panel data set of residential land price. Based on twelve factors in three aspects (land attributes, location factors and neighborhood attributes), an evaluation index system of resident land price was established. The spatial distributions of estimated coefficients and pseudo t-values of three major explanatory variables (floor area ratio, distance to nearest center business district (CBD) and distance to nearest lake), obtained from GWR analysis, indicated that their relationships of the impact factors with land price are spatially non-stationary. The positive impact of floor area ratio on land price is more significant in highly developed areas than in less developed areas. Conversely, the negative impact of distance to nearest CBD on land price is larger in highly developed areas than in less developed areas. Moreover, wealthier dwellers may be willing to pay a higher price for a good lake view (especially views of small lakes), but infrastructure barriers (near some large lakes) cause negative effect. The outputs of this study, which provide detailed information on the relationships between land price and impact factors in local areas, are promising for urban planners to scientifically evaluate land price and make area-specific strategies.

show abstract

Section: Gwr Resultssupporting

confidence: 82%

Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China

Yang

et al. 2016

Applied Geography

122

View full text Add to dashboard Cite

show abstract

“…In addition to the complex relationships between soil attributes and covariates, the number of samples and irregular distribution can negatively affect the predictions. However, the number of samples seems to have less effect on the GWR than the MLR, as shown by Wang et al (2014). The authors conclude that the GWR may be an alternative method for spatial prediction of SOC contents, even with a limited number of samples.…”

Section: Resultsmentioning

confidence: 92%

Mapping Soil Organic Carbon and Organic Matter Fractions by Geographically Weighted Regression

et al. 2018

View full text Add to dashboard Cite

The soil organic matter (SOM) content and dynamic are related to vegetation cover, climate, relief, and geology; these factors have strong variation in space in the southeastern of Brazil. The objective of the study was to compare and evaluate performance of classical multiple linear regressions (MLR) and geographically weighted regression (GWR) models to predict soil organic carbon (SOC) and chemical fractions of organic matter in the Brazilian southeastern mountainous region. The regression models were fitted based on SOC and chemical fractions of SOM. The points ( = 89) were selected by pedologist's experience along transects and toposequences. The covariates were also selected using the empirical knowledge of pedologists when choosing variables that drive soil carbon content and its dynamics. Geology map, legacy soils map, terrain attributes derived from digital elevation model, and remote sensing indices derived from RapidEye sensor bands were used as covariates. In all MLR models (except for fulvic acid fraction [FAF]), the legacy soil map was selected as a covariate by the stepwise approach. The geology map was not selected as important covariate to predict FAF and humin (HUM). At least one variable derived from remote sensing was selected by the adjusted models. For the prediction of the SOC, HUM, and FAF, the GWR models had the highest performance. The MLR models extrapolated the results, especially for SOC. The relationships among SOC, SOM fractions, and environmental covariates were affected by local landscape variability, and the GWR model was better at modeling.

show abstract

“…Respecto al efecto que muestran las variables, se observa en el grupo de las variables topográficas que tienen una notable influencia en la predicción del contenido de la MOS (Berhe, 2012;Doetterl et al, 2012;Wang et al, 2014), y muy especialmente la pendiente (Slp) y las curvaturas (CuP y CuPP) del terreno. En los tres casos existe una relación directa con la disminución de la MOS en las áreas llanas o de escasa pendiente.…”

Section: Discusión Y Conclusionesunclassified

“…Con el fin de avanzar en una cartografía de suelos más detallada a lo largo de los últimos años se han elaborado diferentes técnicas para predecir, sobre grandes extensiones (de aquí en adelante, regionales), el valor de una variable determinada del suelo. En los primeros 1980s se abordó la estimación espacialmente distribuida de variables del suelo mediante la aplicación de técnicas de kriging y cokriging (McBratney et al, 1981), o Regresiones Geográficamente Ponderadas (GWR) (Wang et al, 2013;Wang et al, 2014;Zeng et al, 2016), basadas fundamentalmente en la dependencia espacial de las variables de estudio. La utilidad de estas técnicas es mayor para áreas de extensión media y con muestreos bastante intensivos y regulares (Chen et al 2000;Fox and Sabbagh 2002).…”

Section: Introductionunclassified

Efectos de las variables ambientales en la estimación de materia orgánica del suelo a escala regional en un ambiente semiarido (Región de Murcia, España)

Pérez‐Cutillas¹,

Barberá²,

García³

2017

BAGE

View full text Add to dashboard Cite

RESUMENLa estimación de valores edáficos mediante el empleo de técnicas de modelización espacial, basada en procesos y variables ambientales, es utilizada en este trabajo para analizar el efecto que tienen estas variables sobre la materia orgánica del suelo (MOS). La discusión es enormemente interesante por las conclusiones que se pueden extraer de las distintas implicaciones que ejercen los patrones ecológicos en su distribución y variabilidad espacial. En este estudio se ha utilizado una base de datos de 1126 muestras de materia orgánica en capa arable del suelo en la Región de Murcia (SE de España), con el fin de estimar el contenido de la MOS a escala regional (más de 10000 km 2 ) mediante el uso de 21 variables ambientales como predictores. Entre las variables más influyentes destacan las asociadas al relieve (i.e. la pendiente, las curvaturas del terreno), la acumulación de flujo vinculada a las redes de drenaje, la precipitación, la radicación solar y ciertos índices específicos obtenidos mediante técnicas de teledetección, como el NDVI o el Índice Mineralógico de Arcillas CMI.Palabras clave: Propiedades del suelo, materia orgánica del suelo, modelización, SIG, variables ambientales, teledetección.Boletín de la Asociación de Geógrafos Españoles N.º 75 -2017, págs. 175-191 I.S.S.N.: 0212-9426

show abstract

Mapping soil organic matter with limited sample data using geographically weighted regression

Cited by 19 publications

References 42 publications

Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China

Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China

Mapping Soil Organic Carbon and Organic Matter Fractions by Geographically Weighted Regression

Efectos de las variables ambientales en la estimación de materia orgánica del suelo a escala regional en un ambiente semiarido (Región de Murcia, España)

Contact Info

Product

Resources

About