scholarly journals Spatial Mapping of Soil Depth, Coarse Fragments and Textural Classes of Tamil Nadu Using Digital Soil Mapping Approach

2021 ◽  
Author(s):  
B Kalaiselvi ◽  
S. Dharumarajan ◽  
M. Lalitha ◽  
R. Sriniv ◽  
R. Vasundhara ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Random Forest ◽  
Soil Properties ◽  
Tamil Nadu ◽  
Soil Depth ◽  
Soil Mapping ◽  
Random Forest Model ◽  
Digital Soil Mapping ◽  
Environmental Covariates ◽  
Forest Model

Abstract Knowledge on spatial distribution of soil depth, coarse fragments and texture are crucial for land resource management and environmental soil modeling. Digital soil mapping approach helps in prediction of spatial soil information by establishing the relationship between soil and environmental covariates. In the present study, we assessed spatial distribution of soil depth, coarse fragments (CF) and soil textural classes over 0.13 M sq.km area of Tamil Nadu state. About 2100 samples were used for the prediction of soil properties using random forest model (RF). Out of which, 80 per cent samples were used for training and 20 percent samples were used for testing. Different environmental covariates such as digital elevation model outputs, landsat data and bioclimatic variables were related to predict the soil properties. The predicted soil depth and CF ranged from 46-200 cm and 1-42 per cent respectively. The RF model performed well by explaining the variability (R 2 ) of 43% for soil depth and 21% for coarse fragments with RMSE of 38 cm and 13%, respectively. The RF classifier classified the soil textural classes with 64% overall accuracy and 43% kappa index. Variable importance ranking of Random forest model showed that elevation, MrVBF are the important predictors used for prediction of soil depth and CF, whereas remote sensing vegetation indices such as NDVI, EVI were acted as primary variable for prediction of soil textural classes. In this study, 250 m resolution detailed soil depth, CF and textural class maps were prepared which will be useful for different environmental modeling and proper agricultural management purposes.

scholarly journals Spatial Prediction of the Forest Soil Water Holding Capacities in Temperate Region on a National Scale With Random Forest Models

2021 ◽  
Author(s):  
Hyunje Yang ◽  
Honggeun Lim ◽  
Hyung Tae Choi
Keyword(s):  
Random Forest ◽  
South Korea ◽  
Soil Properties ◽  
Water Content ◽  
Spatial Prediction ◽  
Random Forest Model ◽  
National Scale ◽  
Environmental Covariates ◽  
Forest Model ◽  
Water Holding

Soil water holding capacities (SWHCs) is important input factor in hydrological simulation models for sustainable water management. Forests that covered 63% of South Korea are the main source of clean water, and it is essential to estimate SWHCs on a nationwide scale for effective forest water resources management. However, there are a few studies estimating SWHCs on a nationwide scale in the temperate regions especially in South Korea. Fortunately, forest spatial big data have been collected on a national scale, and the nationwide prediction of the SWHC can be possible with this dataset. In this study, spatial prediction of forest SWHCs (saturated water content, water content at pF1.8 and 2.7) was conducted with 953 forest soil samples and forest spatial big dataset. 4 soil properties and 14 environmental covariates were used for predicting SWHCs. Simple linear regression and random forest model were compared for selecting the optimal predictive model. From the variable importance analysis, environmental covariates had as big importance as soil properties had. And prediction performance of the model with environmental covariates as the input data was higher than that of the model with soil properties. Comparing two models, the random forest model could accurately and stably predict SWHCs than the simple linear model. As a result of spatial prediction of SWHCs at the national scale through the random forest model and the forest spatial big dataset, it was confirmed that higher SWHCs were distributed along with the Baekdudaegan, the watershed-crest-line in South Korea.

scholarly journals Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

2014 ◽  
Vol 38 (3) ◽  
pp. 706-717 ◽  
Author(s):  
Waldir de Carvalho Junior ◽  
Cesar da Silva Chagas ◽  
Philippe Lagacherie ◽  
Braz Calderano Filho ◽  
Silvio Barge Bhering
Keyword(s):  
Soil Properties ◽  
Predictive Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Coefficient Of Determination ◽  
Band Ratio ◽  
Data Set ◽  
Environmental Covariates

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

scholarly journals Evaluation of digital soil mapping approaches with large sets of environmental covariates

2017 ◽  
Author(s):  
Madlene Nussbaum ◽  
Kay Spiess ◽  
Andri Baltensweiler ◽  
Urs Grob ◽  
Armin Keller ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Soil Depth ◽  
Soil Mapping ◽  
Environmental Data ◽  
Digital Soil Mapping ◽  
Boosted Regression Trees ◽  
Gradient Boosting ◽  
Validation Data ◽  
Environmental Covariates ◽  
Large Sets

Abstract. Spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to required soil depth. Conventional soil map generation remains costly. Field based generation of large soil data sets and of conventional soil maps remains costly. Meanwhile, soil legacy data and comprehensive sets of spatial environmental data are available for many regions. Digital soil mapping (DSM) approaches – relating soil data (responses) to environmental data (covariates) – are facing the challenge to build statistical models from large sets of covariates originating for example from airborne imaging spectroscopy or multi-scale terrain analysis. We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping effective soil depth available to plants (SD), pH, soil organic matter (SOM), effective cation exchange capacity (ECEC), clay, silt, gravel content and bulk density for four soil layers (totalling 48 responses). Models were built from 300–500 environmental covariates by selecting linear models by (1) grouped lasso and by an ad-hoc stepwise procedure for (2) robust external-drift kriging (EDK). For (3) geoadditive models we selected penalized smoothing spline terms by componentwise gradient boosting (geoGAM). We further used two tree-based methods: (4) boosted regression trees (BRT) and (5) Random Forest (RF). Lastly, we computed (6) weighted model averages (MA) from predictions obtained from methods 1–5. Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3–6 % of all covariates). To automatically select a sparse trend model for EDK was however difficult, and the applied ad hoc procedure was computationally inefficient and over-fitted the data. Differences in predictive performance, tested on independent validation data, were mostly small and did not reveal a single best method for 48 responses. Nevertheless, RF was on average often best among methods 1–5 (28 of 48 responses), but was outcompeted by MA for 14 of these 28 responses. RF tended to over-fit the data. Performance of BRT was slightly worse than RF. GeoGAM performed poorly on some responses and was only best for 7 of 48 responses. Predictive precision of lasso was intermediate. All models generally had small bias. Only the computationally very efficient lasso had slightly larger bias likely because it tended to under-fit the data. Summarizing, although differences were small, the frequencies of best and worst performance clearly favoured RF if a single method is applied MA if multiple prediction models can be developed.

scholarly journals Quantifying soil and critical zone variability in a forested catchment through digital soil mapping

2014 ◽  
Vol 1 (1) ◽  
pp. 1-49 ◽  
Author(s):  
M. Holleran ◽  
M. Levi ◽  
C. Rasmussen
Keyword(s):  
Soil Properties ◽  
Critical Zone ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Mean Annual Precipitation ◽  
Catchment Scale ◽  
Genetic Horizon ◽  
Geochemical Composition ◽  
Environmental Covariates ◽  
Forested Catchment

Abstract. Quantifying catchment scale soil property variation yields insights into critical zone evolution and function. The objective of this study was to quantify and predict the spatial distribution of soil properties within a high elevation forested catchment in southern AZ, USA using a combined set of digital soil mapping (DSM) and sampling design techniques to quantify catchment scale soil spatial variability. The study focused on a 6 ha catchment on granitic parent materials under mixed-conifer forest, with a mean elevation of 2400 m a.s.l., mean annual temperature of 10 °C and mean annual precipitation of ~ 85 cm yr−1. The sample design was developed using a unique combination of iterative principal component analysis (iPCA) of environmental covariates derived from remotely sensed imagery and topography, and a conditioned Latin Hypercube Sampling (cLHS) scheme. Samples were collected by genetic horizon from 24 soil profiles excavated to the depth of refusal and characterized for soil mineral assemblage, geochemical composition, and general soil physical and chemical properties. Soil properties were extrapolated across the entire catchment using a combination of least squares linear regression between soil properties and selected environmental covariates, and spatial interpolation or regression residual using inverse distance weighting (IDW). Model results indicated that convergent portions of the landscape contained deeper soils, higher clay and carbon content, and greater Na mass loss relative to adjacent slopes and divergent ridgelines. The results of this study indicated that: (i) the coupled application of iPCA and cLHS produced a sampling scheme that captured the majority of catchment scale soil variability; (ii) application of relatively simple regression models and IDW interpolation of residuals described well the variance in measured soil properties and predicted spatial correlation of soil properties to landscape structure; and (iii) at this scaleof observation, 6 ha catchment, topographic covariates explained more variation in soil properties than vegetation covariates. The DSM techniques applied here provide a framework for interpreting catchment scale variation in critical zone process and evolution. Future work will focus on coupling results from this coupled empirical-statistical approach to output from mechanistic, process-based numerical models of critical process and evolution.

scholarly journals Quantifying soil and critical zone variability in a forested catchment through digital soil mapping

SOIL ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 47-64 ◽  
Author(s):  
M. Holleran ◽  
M. Levi ◽  
C. Rasmussen
Keyword(s):  
Soil Properties ◽  
Critical Zone ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Mean Annual Precipitation ◽  
Catchment Scale ◽  
Genetic Horizon ◽  
Geochemical Composition ◽  
Environmental Covariates ◽  
Forested Catchment

Abstract. Quantifying catchment-scale soil property variation yields insights into critical zone evolution and function. The objective of this study was to quantify and predict the spatial distribution of soil properties within a high-elevation forested catchment in southern Arizona, USA, using a combined set of digital soil mapping (DSM) and sampling design techniques to quantify catchment-scale soil spatial variability that would inform interpretation of soil-forming processes. The study focused on a 6 ha catchment on granitic parent materials under mixed-conifer forest, with a mean elevation of 2400 m a.s.l, mean annual temperature of 10 °C, and mean annual precipitation of ~ 85 cm yr−1. The sample design was developed using a unique combination of iterative principal component analysis (iPCA) of environmental covariates derived from remotely sensed imagery and topography, and a conditioned Latin hypercube sampling (cLHS) scheme. Samples were collected by genetic horizon from 24 soil profiles excavated to the depth of refusal and characterized for soil mineral assemblage, geochemical composition, and general soil physical and chemical properties. Soil properties were extrapolated across the entire catchment using a combination of least-squares linear regression between soil properties and selected environmental covariates, and spatial interpolation or regression residual using inverse distance weighting (IDW). Model results indicated that convergent portions of the landscape contained deeper soils, higher clay and carbon content, and greater Na mass loss relative to adjacent slopes and divergent ridgelines. The results of this study indicated that (i) the coupled application of iPCA and cLHS produced a sampling scheme that captured the greater part of catchment-scale soil variability; (ii) application of relatively simple regression models and IDW interpolation of residuals described well the variance in measured soil properties and predicted spatial correlation of soil properties to landscape structure; and (iii) at this scale of observation, 6 ha catchment, topographic covariates explained more variation in soil properties than vegetation covariates. The DSM techniques applied here provide a framework for interpreting catchment-scale variation in critical zone process and evolution. Future work will focus on coupling results from this coupled empirical–statistical approach to output from mechanistic, process-based numerical models of critical zone process and evolution.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

Sign in / Sign up

Export Citation Format

Share Document