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).

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.

Spatial Prediction of Soil Temperature in China

2014 ◽  
Vol 955-959 ◽  
pp. 3718-3723
Author(s):  
Hui Zhi Zhang ◽  
Xue Zheng Shi
Keyword(s):  
Soil Temperature ◽  
Spatial Patterns ◽  
Air Temperature ◽  
Ordinary Kriging ◽  
Spatial Prediction ◽  
Regression Kriging ◽  
Auxiliary Variables ◽  
Geostatistical Methods ◽  
The Mean ◽  
Mean Annual Air Temperature

Temperature affects many soil biochemical and geochemical processes. The growth of plants, seed germination, circulations of carbon and nitrogen are all significantly influenced by soil temperature, thus it is important to estimate the spatial pattern of soil temperature. This paper shows the results of spatial patterns of mean annual soil temperature interpolated from the measurements of 698 meteorological stations in China. Four geostatistical methods, ordinary kriging (OK), regression kriging with mean annual air temperature (RK-1), regression kriging with latitude, longitude and elevation (RK-2) and regression kriging with multi-auxiliary predictors (RK-3), were compared. Ordinary kriging (OK) directly interpolated the mean annual soil temperature data extracted from meteorological stations to obtain the spatial patterns of the mean annual soil temperature. For the three regression kriging methods, intensive auxiliary variables (mean annual air temperature, elevation, latitude and longitude), which were correlated with mean annual soil temperature, were used to increase the accuracy of estimation. The results suggested that RK-3 preformed best, followed by RK-1 and RK-2. The intensive data of auxiliary variables used in the regression kriging significantly improved the accuracy of interpolation results.

scholarly journals Resources Estimation on further Exploration activities in PT. Trimegah Bangun Persada (Harita Group) Kawasi Village, South Halmahera District, North Maluku

2021 ◽  
Vol 1 (2) ◽  
pp. 29-41
Author(s):  
Simela Talaohu
Keyword(s):  
Ordinary Kriging ◽  
Statistical Approach ◽  
Nearest Neighbor ◽  
Estimation Methods ◽  
The North ◽  
North Part ◽  
Block Area ◽  
Inverse Distance ◽  
Space Distance ◽  
Exploration Activity

PT. Trimegah Bangun Persada intends to do mining activity at the north part of IUP as the south and west parts have already been mined. This research aimed at producing natural resources of laterite nickel in the advanced exploration activity of Tangkuban Block. Besides, it also compared and determined the estimation methods having a good correlation with drilling results. Tangkuban Block carried out core drilling within total drill numbers of 286 spots and space distance 25 meters at the block area 22.16 Ha. This research began with determining the geological domain consisting of limonite, saprolite, and bedrock through a geo-statistical approach. After that, the researcher estimated nickel resources by three methods namely ordinary kriging, inverse distance weight, and nearest neighbor point. The result of estimation by ordinary kriging obtained a total volume of limonite layer 1,345,313 m3 with the content average of 1%, while the total volume of saprolite layer was 1,850,000 m3 1.64%.

scholarly journals Wet-season spatial variability of N<sub>2</sub>O emissions from a tea field in subtropical central China

2015 ◽  
Vol 12 (2) ◽  
pp. 1475-1508
Author(s):  
X. Fu ◽  
X. Liu ◽  
Y. Li ◽  
J. Shen ◽  
Y. Wang ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Central China ◽  
N2o Emissions ◽  
Regression Kriging ◽  
Wet Season ◽  
Interpolation Methods ◽  
N2o Fluxes ◽  
Tea Field

Abstract. Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate estimations of N2O emissions from tea-planted soils is challenging due to strong spatial variability. We examined the spatial variability of N2O emissions from a red-soil tea field in Hunan province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers approximately regular gridded in a 4.0 ha tea field. The N2O fluxes for a 30 min snapshot (10–10.30 a.m.) ranged from −1.73 to 1659.11 g N ha−1 d−1 and were positively skewed with an average flux of 102.24 g N ha−1 d−1. The N2O flux data were transformed to a normal distribution by using a logit function. The geostatistical analyses of our data indicated that the logit-transformed N2O fluxes (FLUX30t) exhibited strong spatial autocorrelation, which was characterized by an exponential semivariogram model with an effective range of 25.2 m. As observed in the wet season, the logit-transformed soil ammonium-N (NH4Nt), soil nitrate-N (NO3Nt), soil organic carbon (SOCt), total soil nitrogen (TSNt) were all found to be significantly correlated with FLUX30t (r=0.57–0.71, p<0.001). Three spatial interpolation methods (ordinary kriging, regression kriging and cokriging) were applied to estimate the spatial distribution of N2O emissions over the study area. Cokriging with NH4Nt and NO3Nt as covariables (r= 0.74 and RMSE =1.18) outperformed ordinary kriging (r= 0.18 and RMSE =1.74), regression kriging with the sample position as a predictor (r= 0.49 and RMSE =1.55) and cokriging with SOCt as a covariable (r= 0.58 and RMSE =1.44). The predictions of the three kriging interpolation methods for the total N2O emissions of the 4.0 ha tea field ranged from 148.2 to 208.1 g N d−1, based on the 30 min snapshots obtained during the wet season. Our findings suggested that to accurately estimate the total N2O emissions over a region, the environmental variables (e.g., soil properties) and the current land use pattern (e.g., tea row transects in the present study) must be included in spatial interpolation. Additionally, compared with other kriging approaches, the cokriging prediction approach showed great advantages in being easily deployed, and more importantly providing accurate regional estimation of N2O emissions from tea-planted soils.

scholarly journals Wet-season spatial variability in N<sub>2</sub>O emissions from a tea field in subtropical central China

2015 ◽  
Vol 12 (12) ◽  
pp. 3899-3911 ◽  
Author(s):  
X. Fu ◽  
X. Liu ◽  
Y. Li ◽  
J. Shen ◽  
Y. Wang ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Central China ◽  
N2o Emissions ◽  
Regression Kriging ◽  
Wet Season ◽  
Interpolation Methods ◽  
N2o Fluxes ◽  
Tea Field

Abstract. Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate estimations of N2O emissions from tea-planted soils is challenging due to strong spatial variability. We examined the spatial variability in N2O emissions from a red-soil tea field in Hunan Province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers approximately regular gridded in a 4.0 ha tea field. The N2O fluxes for a 30 min snapshot (10:00–10:30 a.m.) ranged from −1.73 to 1659.11 g N ha−1 d−1 and were positively skewed with an average flux of 102.24 g N ha−1 d−1. The N2O flux data were transformed to a normal distribution by using a logit function. The geostatistical analyses of our data indicated that the logit-transformed N2O fluxes (FLUX30t) exhibited strong spatial autocorrelation, which was characterized by an exponential semivariogram model with an effective range of 25.2 m. As observed in the wet season, the logit-transformed soil ammonium-N (NH4Nt), soil nitrate-N (NO3Nt), soil organic carbon (SOCt) and total soil nitrogen (TSNt) were all found to be significantly correlated with FLUX30t (r = 0.57–0.71, p < 0.001). Three spatial interpolation methods (ordinary kriging, regression kriging and cokriging) were applied to estimate the spatial distribution of N2O emissions over the study area. Cokriging with NH4Nt and NO3Nt as covariables (r = 0.74 and RMSE = 1.18) outperformed ordinary kriging (r = 0.18 and RMSE = 1.74), regression kriging with the sample position as a predictor (r = 0.49 and RMSE = 1.55) and cokriging with SOCt as a covariable (r = 0.58 and RMSE = 1.44). The predictions of the three kriging interpolation methods for the total N2O emissions of 4.0 ha tea field ranged from 148.2 to 208.1 g N d−1, based on the 30 min snapshots obtained during the wet season. Our findings suggested that to accurately estimate the total N2O emissions over a region, the environmental variables (e.g., soil properties) and the current land use pattern (e.g., tea row transects in the present study) must be included in spatial interpolation. Additionally, compared with other kriging approaches, the cokriging prediction approach showed great advantages in being easily deployed and, more importantly, providing accurate regional estimation of N2O emissions from tea-planted soils.

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