scholarly journals Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach

2020 ◽  
Vol 12 (22) ◽  
pp. 9330
Author(s):  
Tao Liu ◽  
Huan Zhang ◽  
Tiezhu Shi
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Spatial Interpolation ◽  
Weighted Regression ◽  
Regression Kriging ◽  
Carbon Density ◽  
Soil Organic Carbon Density

Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km2) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R2), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.

Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon

2017 ◽  
Vol 20 ◽  
pp. 76-91 ◽  
Author(s):  
Huichun Ye ◽  
Wenjiang Huang ◽  
Shanyu Huang ◽  
Yuanfang Huang ◽  
Shiwen Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Regression Kriging

Spatial modelling of soil organic carbon stocks with combined principal component analysis and geographically weighted regression

2018 ◽  
Vol 156 (6) ◽  
pp. 774-784 ◽  
Author(s):  
Long Guo ◽  
Mei Luo ◽  
Chengsi Zhangyang ◽  
Chen Zeng ◽  
Shanqin Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Prediction Models ◽  
Principal Component ◽  
Weighted Regression ◽  
Auxiliary Variables ◽  
Soc Stocks

AbstractWith the development of remote sensing and geostatistical technology, complex environmental variables are increasingly easily quantified and applied in modelling soil organic carbon (SOC). However, this emphasizes data redundancy and multicollinearity problems adding to the difficulty in selecting dominant influential auxiliary variables and uncertainty in estimating SOC stocks. The current paper considers the spatial characteristics of SOC density (SOCD) to construct prediction models of SOCD on the basis of reducing the data dimensionality and complexity using the principal component analysis (PCA) method. A total of 260 topsoil samples were collected from Chahe town, China. Eight environmental variables (elevation, aspect, slope, normalized difference vegetation index, normalized difference moisture index, nearest distance to construction area and road, and land use degree comprehensive index) were pre-analysed by PCA and then extracted as the main principal component variables to construct prediction models. Two geostatistical approaches (ordinary kriging and ordinary co-kriging) and two regression approaches (ordinary least squares and geographically weighted regression (GWR)) were used to estimate SOCD. Results showed that PCA played an important role in reducing the redundancy and multicollinearity of the auxiliary variables and GWR achieved the highest prediction accuracy in these four models. GWR considered not only the spatial characteristics of SOCD but also the related valuable information of the auxiliary attributes. In summary, PCA-GWR is a promising spatial method used here to predict SOC stocks.

Predicting soil organic carbon density using auxiliary environmental variables in northern Iran

2015 ◽  
Vol 62 (3) ◽  
pp. 375-393 ◽  
Author(s):  
Samereh Falahatkar ◽  
Seyed Mohsen Hosseini ◽  
Shamsollah Ayoubi ◽  
Abdolrassoul Salmanmahiny
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Environmental Variables ◽  
Carbon Density ◽  
Northern Iran ◽  
Soil Organic Carbon Density

Estimating soil organic carbon density in plains using landscape metric-based regression Kriging model

2019 ◽  
Vol 195 ◽  
pp. 104381 ◽  
Author(s):  
Zihao Wu ◽  
Bozhi Wang ◽  
Junlong Huang ◽  
Zihao An ◽  
Ping Jiang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Kriging Model ◽  
Regression Kriging ◽  
Carbon Density ◽  
Landscape Metric ◽  
Soil Organic Carbon Density

Effects of land use and topography on spatial variety of soil organic carbon density in a hilly, subtropical catchment of China

Soil Research ◽  
2017 ◽  
Vol 55 (2) ◽  
pp. 134 ◽  
Author(s):  
Huanyao Liu ◽  
Jiaogen Zhou ◽  
Qingyu Feng ◽  
Yuyuan Li ◽  
Yong Li ◽  
...  
Keyword(s):  
Land Use ◽  
Environmental Factors ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Paddy Fields ◽  
Accurate Estimation ◽  
Carbon Density ◽  
Topographic Wetness Index ◽  
Soil Organic Carbon Density ◽  
Effects Of Land Use

A good understanding the effects of environmental factors on the spatial variety of soil organic carbon density (SOCD) helps achieve a relatively accurate estimation of the soil organic carbon stock of terrestrial ecosystems. The present study analysed the SOCD of 1033 top soil samples (0–20cm) from the Jinjing catchment located in subtropical China. Spatial variability of SOCD was estimated using a geostatistics method and a geographically weighted regression (GWR) model, and the major environmental factors affecting SOCD were also explored. In the present study, SOCD had a moderate spatial dependence and the best-fitting model was exponential with a nugget-to-sill ratio of 60.72% and a range of 182m. Land use types (woodlands, paddy fields and tea fields) and topography (elevation, slope, topographic wetness index (TWI)) affected the spatial variation of SOCD. Mean SOCD in the paddy fields was higher than in woodland and tea fields (3.50 vs 3.24 and 2.81kgCm–2 respectively; P<0.05). In addition, SOCD was generally higher in the valleys of paddy fields (with low slope and high TWI) and the hills of woodland (with high elevation and increased slope). GWR generated the spatial distribution of SOCD more accurately than ordinary kriging, inverse distance weighted, multiple linear regression model, and linear mixed-effects model. The results of the present study could enhance our understanding of the effects of land use and topography on SOCD, and improve the accuracy in predicting SOCD by GWR in small catchments of complex land use and topography.

scholarly journals Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon.

2020 ◽  
Author(s):  
Marlvin Anemey Tewara ◽  
Liu Yunxia ◽  
Weiqiang Ling ◽  
Binang Helen Barong ◽  
Prisca Ngetemalah Mbah-Fongkimeh ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Spatial Variability ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Spatial Association ◽  
Ordinary Least Squares ◽  
Variable Coefficients ◽  
Weighted Regression ◽  
Gwr Model

Abstract Background: Studies have illustrated the association of malaria cases with environmental factors in Cameroon but limited in addressing how these factors vary in space for timely public health interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique.Methods: The global Ordinary least squares (OLS) in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the global OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Results: The OLS candidate environmental variable coefficients were statistically significant (adjusted R2 = 22.3% and p < 0.01) for a properly specified GWR model. The GWR model identified a strong spatial association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water with an overall model performance of 0.243 (adjusted R2= 24.3%).Conclusion: The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon, there should be the creation of malaria outreach programs and further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases.

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