Kooperativer Bibliotheksverbund

Berlin Brandenburg

and
and

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
Type of Medium
Language
Year
  • 1
    Language: English
    In: Spatial Statistics, August 2015, Vol.13, pp.106-122
    Description: With respect to sampling for regression-based digital soil mapping (DSM), the above all aim is to ensure that the spatial variability of the soil is well-captured without introducing any bias, while the design remains feasible according to operational constraints such as accessibility, man power and cost. Representativeness of the sample concerning the population to be sampled needs to be guaranteed in any regression-based modelling approach. Four selected sampling designs were adapted to show that basically any design may be optimised to represent the -dimensional predictor space of a particular area, while selecting points is only permitted from a small accessible sub-area or from outside the area. Sampling efficiency may be evaluated based on the representation of the predictor space. However, not only each predictor’s probability function but also the interaction between predictors may have to be considered, to select a representative sample. Instead of sampling a previously un-sampled area with limited accessibility, the four sampling designs may also be used to subsample an existing dataset and, thereby, optimise a suboptimal dataset based on the predictor space of the area which shall be mapped by DSM.
    Keywords: Sampling Design ; Digital Soil Mapping ; Regression ; Statistics
    ISSN: 2211-6753
    E-ISSN: 2211-6753
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    In: PLoS ONE, 2016, Vol.11(4)
    Description: Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m -2 , displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.
    Keywords: Research Article ; Physical Sciences ; Research And Analysis Methods ; Biology And Life Sciences ; Computer And Information Sciences ; Biology And Life Sciences ; Computer And Information Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Ecology And Environmental Sciences ; Research And Analysis Methods ; Physical Sciences ; Biology And Life Sciences ; Earth Sciences ; Biology And Life Sciences ; Computer And Information Sciences
    E-ISSN: 1932-6203
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    Language: English
    In: Applied and Environmental Soil Science, 2014
    Description: A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow's Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders) and landslides (mixing up mineral soil horizons on slopes).
    Keywords: Digital Mapping – Analysis ; Machine Learning – Usage ; Soil Surveys – Analysis
    ISSN: 1687-7667
    Source: Cengage Learning, Inc.
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Language: English
    In: Applied and Environmental Soil Science, 2014
    Description: The sloping mire landscape of the investigation area, in the southern Andes of Ecuador, is dominated by stagnic soils with thick organic layers. The recursive partitioning algorithm Random Forest was used to predict the spatial water stagnation pattern and the thickness of the organic layer from terrain attributes. Terrain smoothing from 10 to 30 m raster resolution was applied in order to obtain the best possible model. For the same purpose, several model tuning parameters were tested and a prepredictor selection with the R-package Boruta was applied. Model versions were evaluated and compared by 100 repetitions of the calculation of the residual mean square error of a five-fold cross-validation. Position specific density functions of the predicted soil parameters were then used to display prediction uncertainty. Prepredictor selection and tuning of the Random Forest algorithm in some cases resulted in an improved model performance. We therefore recommend testing prepredictor selection and tuning to make sure that the best possible model is chosen. This needs particular emphasis in complex tropical mountain soil-landscapes which provide a real challenge to any soil mapping approach but where Random Forest has proven to be successful due to the testing of model tuning and prepredictor selection.
    Keywords: Landscape Ecology – Analysis
    ISSN: 1687-7667
    Source: Cengage Learning, Inc.
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    In: Applied and Environmental Soil Science, 2014, Vol.2014, 12 pages
    Description: A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders) and landslides (mixing up mineral soil horizons on slopes).
    Keywords: Lineare Regression ; Prädiktor ; Rückbildung ; Methodenvergleich ; Prädiktion ; Feinstaub ; Mineral ; Statistische Methode ; Agriculture;
    ISSN: 1687-7667
    E-ISSN: 1687-7675
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    Language: English
    In: PLoS ONE, 01 January 2017, Vol.12(8), p.e0183205
    Description: Nitrogen (N) and phosphorus (P) in topsoils are critical for plant nutrition. Relatively little is known about the spatial patterns of N and P in the organic layer of mountainous landscapes. Therefore, the spatial distributions of N and P in both the organic layer and the A horizon were analyzed using a light detection and ranging (LiDAR) digital elevation model and vegetation metrics. The objective of the study was to analyze the effect of vegetation and topography on the spatial patterns of N and P in a small watershed covered by forest in South Korea. Soil samples were collected using the conditioned latin hypercube method. LiDAR vegetation metrics, the normalized difference vegetation index (NDVI), and terrain parameters were derived as predictors. Spatial explicit predictions of N/P ratios were obtained using a random forest with uncertainty analysis. We tested different strategies of model validation (repeated 2-fold to 20-fold and leave-one-out cross validation). Repeated 10-fold cross validation was selected for model validation due to the comparatively high accuracy and low variance of prediction. Surface curvature was the best predictor of P contents in the organic layer and in the A horizon, while LiDAR vegetation metrics and NDVI were important predictors of N in the organic layer. N/P ratios increased with surface curvature and were higher on the convex upper slope than on the concave lower slope. This was due to P enrichment of the soil on the lower slope and a more even spatial distribution of N. Our digital soil maps showed that the topsoils on the upper slopes contained relatively little P. These findings are critical for understanding N and P dynamics in mountainous ecosystems.
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 7
    In: Applied and Environmental Soil Science, 2014, Vol.2014, 10 pages
    Description: The sloping mire landscape of the investigation area, in the southern Andes of Ecuador, is dominated by stagnic soils with thick organic layers. The recursive partitioning algorithm Random Forest was used to predict the spatial water stagnation pattern and the thickness of the organic layer from terrain attributes. Terrain smoothing from 10 to 30 m raster resolution was applied in order to obtain the best possible model. For the same purpose, several model tuning parameters were tested and a prepredictor selection with the R-package Boruta was applied. Model versions were evaluated and compared by 100 repetitions of the calculation of the residual mean square error of a five-fold cross-validation. Position specific density functions of the predicted soil parameters were then used to display prediction uncertainty. Prepredictor selection and tuning of the Random Forest algorithm in some cases resulted in an improved model performance. We therefore recommend testing prepredictor selection and tuning to make sure that the best possible model is chosen. This needs particular emphasis in complex tropical mountain soil-landscapes which provide a real challenge to any soil mapping approach but where Random Forest has proven to be successful due to the testing of model tuning and prepredictor selection.
    Keywords: Agriculture;
    ISSN: 1687-7667
    E-ISSN: 1687-7675
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    Language: English
    In: Catena, October 2012, Vol.97, pp.20-30
    Description: The World Reference Base for Soil Resources (WRB) (FAO, IUSS Working Group WRB, 2007) at present does not acknowledge the spatial soil continuum, but provides a sound basis to do so. Using methods from statistical learning theory to develop digital soil maps is much more efficient and precise while regionalising soil diagnostic properties instead of complex entities such as the soil units assigned by the WRB. Particularly in providing spatial soil information displayed in digital soil maps, any aggregation of this spatial soil information to soil units means a loss of information. The soil landscape can be systematically described in its spatial continuum simply by the vertical order and extent of the WRB diagnostic horizons. The diagnostic horizons are related in their thickness to a standard depth and listed from top to bottom in order of appearance. Typical diagnostic horizon thickness and occurrence probability were predicted from terrain parameters by classification and regression trees (CART), throughout the research area in southern Ecuador. The two disadvantages of CART, abrupt prediction class boundaries and dependence on the dataset, were addressed by hundredfold model runs on different data subsets, leading to a range of possible predictions. Prediction uncertainty was included in the digital soil maps by calculating these predictions' means and standard deviations as well as by horizon occurrence probability prediction. Model performance was evaluated by means of hundredfold external cross validation. Terrain parameters were found to have a strong influence on diagnostic topsoil properties. However, no influence on the vertical profile differentiation was observed. Hence predicting horizon thickness and subsoil diagnostic properties was difficult. The systematic description of the soil continuum of this particular soil-landscape resulted in histic and stagnic soil parts dominating the first 100 cm of the soil column for most of the area. ► Systematic description of the soil continuum using the WRB diagnostic horizons. ► Development of digital soil maps of a mountain area in south Ecuador. ► Map uncertainty is included due to multiple model runs on Jackknife partitions..
    Keywords: Soil Classification ; Digital Soil Map ; Jackknife ; Classification and Regression Trees ; Sciences (General) ; Geography ; Geology
    ISSN: 0341-8162
    E-ISSN: 1872-6887
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 9
    Language: English
    In: Geoderma, 15 January 2012, Vol.170, pp.70-79
    Description: Within the southern Ecuadorian Andes, landslides have an impact on landscape development. Landslide risk estimation as well as hydrological modelling requires physical soil data. Statistical models were adapted to predict the spatial distribution of soil texture from terrain parameters. For this purpose, 56 soil profiles were analysed horizon-wise by pipette and laser method. Results by pipette compared to laser method showed the expected shift to higher silt and lower clay contents. Linear regression equations were adapted. The performance of regression tree (RT) and Random Forest (RF) models was compared by hundredfold model runs on random Jackknife partitions. Digital soil maps of sand, silt and clay percentage mean and standard deviation indicate model variability and prediction uncertainty. RF models performed better than RT models. All terrain factors considered in the analysis influenced soil texture of the surface horizon, but altitude a.s.l. was assigned the highest variable importance during model construction. Shallow subsurface flow is considered responsible for increasing sand/clay ratios with increasing altitude, on steep slopes and with overland flow distance to the channel network by removing clay particles downslope. Deeper soil layers are not influenced by this process and therefore, did not show the same texture properties. However, the influence of parent material and landslides on the spatial distribution of soil texture cannot be neglected. Model performance, most probably, could be improved by a bigger dataset. ► Soil texture is predicted on a landscape scale. ► Regression Trees and Random Forest models are compared in their performance. ► The digital soil maps include model variability and prediction uncertainty. ► Model dependence on the dataset is addressed by model runs with various data subsets. ► Surface processes are distinguished from the influence of parent material.
    Keywords: Random Forest ; Regression Tree ; Gis ; Soil Texture ; Tropical Soils ; Agriculture
    ISSN: 0016-7061
    E-ISSN: 1872-6259
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 10
    Language: English
    In: Geomorphology, 2011, Vol.132(3), pp.287-299
    Description: Landslides are a common phenomenon within the Ecuadorian Andes and have an impact on soil-landscape formation. Landslide susceptibility was determined in a steep mountain forest region in Southern Ecuador. Soil mechanical and hydrological properties in addition to terrain steepness were hypothesised to be the major factors in causing soil slides. Hence, the factor of safety ( ) was calculated as the soil shear ratio that is necessary to maintain the critical state equilibrium on a potential sliding surface. Regression tree (RT) and Random Forest (RF) models were compared in their predictive force to regionalise the depth of the failure plane and soil bulk density based on terrain parameters. The depth of the failure plane was assumed at the lower boundary of the stagnic soil layer or soil depth respectively, depending on soils being stagnic or non-stagnic. was determined in dependence of soil wetness referring to 0.001, 0.01, 0.1 and 3 mm h net rainfall rates. Sites with ≥ 1 at 3 mm h (complete saturation) were classified as unconditionally stable; sites with 〈 1 at 0.001 mm h as unconditionally unstable. Bulk density and the depth of the failure plane were regionalised with RF which performed better than RT. Terrain parameters explained the spatial distribution of soil bulk density and the depth of the failure plane only to a relatively small extent which is reasonable due to frequent translocation of soil material by landslides. Nevertheless, their prediction uncertainty still allowed for a reasonable prediction of unconditionally unstable sites. ► Landslide risk was predicted from soil parameters in dependence on soil wetness. ► Landslide occurrence leads to high uncertainty in soil regionalisation. ► Landslide risk depends on the weight burdening the failure plane and slope steepness. ► Unconditionally unstable sites were successfully tested for plausibility. ► Small zones of water saturation trigger landslides.
    Keywords: Factor of Safety ; Failure Plane ; Random Forest ; Regression Tree ; Geography ; Geology
    ISSN: 0169-555X
    E-ISSN: 1872-695X
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages