Mapping the Spatial Variability of Soil Properties: A Comparative Study of Spatial Interpolation Methods in Northeast China

2014 ◽  
Vol 535 ◽  
pp. 483-488 ◽  
Author(s):  
Xiao Yan Li ◽  
Hong Li ◽  
Hui Jia Liu
Keyword(s):  
Soil Properties ◽  
Human Activity ◽  
Ordinary Kriging ◽  
Northeast China ◽  
Prediction Methods ◽  
Regression Kriging ◽  
Hybrid Techniques ◽  
Soil Variables ◽  
Weight Method ◽  
Inverse Distance

We analyzed the variance characteristics of soil organic matter (SOM), total nitrogen (TN), extractable phosphorus (EP), and extractable potassium (EK), in Jiutai County, Northeast China, and compared different prediction methods for mapping of these four soil variables. The prediction methods used were geostatistical interpolation (ordinary kriging), inverse distance weight method, and the hybrid techniques (regression-kriging). A modified jackknifing method involving 40% partitions was used to examine the stability of validate the indices. Root mean square error (RMSE) was used as validation index, and mean RMSE was used to judge the prediction quality. The results showed that the hybrid interpolation regression-kriging cant be used in the region influenced by frequent and high-intensity human activity when the relationship between soil properties and environment factors were not obvious. The ordinary kriging was found to be the best method to fit the experimental semivariogram of SOM and EK. The inverse distance weight method fit well to predict the distribution of TN and EP. For SOM and EK, results showed that data values in the western part were higher than those in the eastern part. However, for TN and EP, there is no clear trend. Water and tillage erosion caused by human activity has weakened the structural influence and elevation and slope played key roles in the distribution of soil variables in the local area.

MENENTUKAN METODE INTERPOLASI SPASIAL CURAH HUJAN BULANAN TERBAIK DI JAWA TIMUR

2021 ◽  
pp. 263
Author(s):  
Andang Kurniawan ◽  
Erwin Makmur ◽  
Supari Supari
Keyword(s):  
Ordinary Kriging ◽  
Regression Kriging ◽  
Inverse Distance

Informasi spasial curah hujan dibutuhkan oleh berbagai sektor namun karena keterbatasan pengamatan, proses interpolasi harus dilakukan. Metode interpolasi spasial terbaik untuk suatu tempat perlu ditentukan secara khusus. Penggunaan metode interpolasi Inverse Distance Weight (IDW) P=5 di Stasiun Klimatologi Malang perlu dikaji ulang. Tujuan penelitian ini adalah mencari justifikasi parameter interpolasi, membandingkan hasil interpolasi, dan pada akhirnya menentukan metode interpolasi terbaik untuk curah hujan bulanan Jawa Timur. Tiga metode yang diperbandingkan adalah IDW, Ordinary Kriging (OK), dan Regression Kriging (RK). Data curah hujan bulanan yang digunakan adalah 197 titik selama 204 bulan. Prediktor RK menggunakan ketinggian, kelerengan, dan estimasi curah hujan satelit. Parameter interpolasi seperti ukuran piksel, jumlah pencarian (NN), model variogram, dan power IDW dijustifikasi terlebih dahulu. Korelasi spasial digunakan untuk membandingkan hasil interpolasi. Validasi silang lipat sepuluh digunakan untuk menghasilkan galat. Galat interpolasi yang digunakan berupa nilai dan selisih kategori warna peta standar. RMSE dan MAE digunakan sebagai parameter validasi. Analisis waktu komputasi juga dilakukan. Piranti lunak R Statistics dan QGIS digunakan untuk membentuk bahan maupun mencari parameter interpolasi sedangkan interpolasi dilakukan menggunakan SAGA. Parameter interpolasi ditentukan sebagai berikut: ukuran piksel=0,01; NN=9; model variogram sperikal dengan Nugget=0, Sill=1, dan range bervariasi; power IDW=1,5. Hasil interpolasi RK jauh berbeda dari IDW maupun OK. Secara umum, IDW memiliki galat paling kecil (MAE kategori=0,871) dibandingkan OK (0,890) maupun RK (1,188).

scholarly journals Assessment of Spatial Variability and Mapping of Soil Texture in Western of Syria using Geographic Information System Techniques (GIS)

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Khozama Barakat AL-Saleh
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Texture ◽  
Texture Mapping ◽  
Cropping Patterns ◽  
Digital Maps ◽  
Silty Loam ◽  
Weight Method ◽  
Clay Percentage ◽  
Inverse Distance

The study aimed to study the spatial variability of soil texture for soils around AL-Basel Lake in Safita Region which located in Tartous Governorate west of Syrian Arab Republic. Spatial maps of soil properties are invaluable in agricultural Production for assessing soil quality, planning land use and determining the suitability of cropping patterns. Geostatistics has been extensively used for quantifying the spatial pattern of soil properties and Inverse Distance Weight technique are proving sufficiently robust for estimating values at unsampled locations. The experiment was conducted on the soils of villages around AL-Basel lake The aim of this work is to study the spatial variability of soil texture. For this purpose ,90 Samples were collected. Results of Laboratory analysis of studied indexes were imported into ArcGis9.3 software and presented in form of Digital Maps that show the spatial Distribution of soil texture using Geostatistical Analyst and Inverse Distance Weight method was used in the Spatial Interpolation. Results of texture analysis showed that the soils have a texture between Loam and Silty Loam. Geostatistical analyst was used for unsampled points. Since 70.94 % of study area has Sand percentage (30-40) %, for Silt 57.73 % of study area has Silt percentage (40-50)% ,for Clay 71.97% of study area has Clay percentage (10-20)%,81.76% of study area has loam texture. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in areas using.

scholarly journals Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

2019 ◽  
Vol 8 (3) ◽  
pp. 147 ◽  
Author(s):  
Tung Gia Pham ◽  
Martin Kappas ◽  
Chuong Van Huynh ◽  
Linh Hoang Khanh Nguyen
Keyword(s):  
Land Use ◽  
Soil Properties ◽  
Soil Ph ◽  
Ordinary Kriging ◽  
Environmental Variables ◽  
Agricultural Land ◽  
Regression Kriging ◽  
Topographic Wetness Index ◽  
Central Vietnam ◽  
Hilly Region

Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions. This study was conducted in a hilly region of Central Vietnam with the following objectives: (i) to evaluate the best environmental variables to estimate soil organic carbon (SOC), total nitrogen (TN), and soil reaction (pH) with a regression kriging (RK) model, and (ii) to compare the accuracy of the ordinary kriging (OK) and RK methods. SOC, TN, and soil pH data were measured at 155 locations within the research area with a sampling grid of 2 km × 2 km for a soil layer from 0 to 30 cm depth. From these samples, 117 were used for interpolation, and the 38 randomly remaining samples were used for evaluating accuracy. The chosen environmental variables are land use type (LUT), topographic wetness index (TWI), and transformed soil adjusted vegetation index (TSAVI). The results indicate that the LUT variable is more effective than TWI and TSAVI for determining TN and pH when using the RK method, with a variance of 7.00% and 18.40%, respectively. In contrast, a combination of the LUT and TWI variables is the best for SOC mapping with the RK method, with a variance of 14.98%. The OK method seemed more accurate than the RK method for SOC mapping by 3.33% and for TN mapping by 10% but the RK method was found more precise than the OK method for soil pH mapping by 1.81%. Further selection of auxiliary variables and higher sampling density should be considered to improve the accuracy of the RK method.

Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties

2014 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Set Foong Ng ◽  
Pei Eng Ch’ng ◽  
Yee Ming Chew ◽  
Kok Shien Ng
Keyword(s):  
Soil Properties ◽  
Lagrange Multipliers ◽  
Unbiased Estimator ◽  
Soil Property ◽  
Minimum Variance ◽  
Distance Method ◽  
True Value ◽  
A Site ◽  
Method Of Lagrange Multipliers ◽  
Inverse Distance

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

scholarly journals Associations between human impacts and forest soil microbial communities

Elem Sci Anth ◽  
2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Yongjian Chen ◽  
Jialiang Kuang ◽  
Pandeng Wang ◽  
Wensheng Shu ◽  
Albert Barberán
Keyword(s):  
Microbial Communities ◽  
Forest Soil ◽  
Human Activity ◽  
Eastern China ◽  
Soil Microbial Communities ◽  
Nitrifying Bacteria ◽  
Rrna Gene ◽  
Internal Transcribed Spacer Region ◽  
Soil Microbial ◽  
Soil Variables

We are living in a new epoch—the Anthropocene, in which human activity is reshaping global biodiversity at an unprecedented rate. Increasing efforts are being made toward a better understanding of the associations between human activity and the geographic patterns in plant and animal communities. However, similar efforts are rarely applied to microbial communities. Here, we collected 472 forest soil samples across eastern China, and the bacterial and fungal communities in those samples were determined by high-throughput sequencing of 16S rRNA gene and internal transcribed spacer region, respectively. By compiling human impact variables as well as climate and soil variables, our goal was to elucidate the association between microbial richness and human activity when climate and soil variables are taken into account. We found that soil microbial richness was associated with human activity. Specifically, human population density was positively associated with the richness of bacteria, nitrifying bacteria and fungal plant pathogens, but it was negatively associated with the richness of cellulolytic bacteria and ectomycorrhizal fungi. Together, these results suggest that the associations between geographic variations of soil microbial richness and human activity still persist when climate and soil variables are taken into account and that these associations vary among different microbial taxonomic and functional groups.

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