The Soil Resource

Jenny, Hans

doi:10.1007/978-1-4612-6112-4

Cited by 769 publications

(98 citation statements)

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“…To simplify the function and better understand soil formation, studies have focused on identifying transitions in soil properties along sequences of soils related to one environmental factor; such as climate (climosequences), time (chronosequences), relief (toposequences), etc. (Jenny, 1980;Birkeland, 1999).…”

Section: Soil: S = F(c O R P T) Simply Said Soil (S) Is a Functmentioning

confidence: 99%

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Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah

Stum

2010

Digital Soil Mapping

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Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART).However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independently because each tree is trained with a new randomly selected bootstrap sample, and a random subset of variables is used to split each node. To train and validate the RF trees, 561 soil descriptions were made in the field. An additional 111 points were added by case-based reasoning using aerial photo interpretation. As RF makes classification decisions from the mode of many independently grown trees, model iii uncertainty can be derived. The overall out of the bag (OOB) error was lower without weighting of classes; weighting increased the overall OOB error and the resulting output did not reflect soil-landscape relationships observed in the field. The final RF model had an OOB error of 55.2% and predicted soils on landforms consistent with soil-landscape relationships. The OOB error for individual classes typically decreased with increasing class size. In addition to the final classification, I determined the second and third most likely classification, model confidence, and the hypothetical extent of individual classes.Pixels that had high possibility of belonging to multiple soil classes were aggregated using a minimum confidence value based on limiting soil features, which is an effective and objective method of determining membership in soil map unit associations and complexes mapped at the 1:24,000 scale. Variables derived from both DEM and Landsat 7 ETM+ sources were important for predicting soil classes based on Gini and standard measures of variable importance and OOB errors from groves grown with exclusively DEM-or Landsat-derived data. Random forests was a powerful predictor of soil classes and produced outputs that facilitated further understanding of soil-landscape relationships.(147 pages) iv ACKNOWLEDGMENTS

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Section: Soil: S = F(c O R P T) Simply Said Soil (S) Is a Functmentioning

confidence: 99%

“…Soil depth and soil production vary with slope and curvature (Heimsath et al, 1997). Aspect is an important control of soil microclimate and vegetation (Jenny, 1980 …”

Section: Relief (R)mentioning

confidence: 99%

Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah

Stum

2010

Digital Soil Mapping

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“…The depth of precipitation varies for each soil, and is controlled by soil moisture and texture (Gile et al, 1966;Machette, 1985). Carbonate supersaturation due to soil drying during évapotranspiration is generally considered the primary mechanism for CaCOs precipitation (e.g., Jenny, 1980;Cerling, 1984;Quade et al, 1989b), which typically occurs in well-drained soils in the vadose or unsaturated zone above the influence of the groundwater table (Fig. 4A).…”

Section: Carbonate Formationmentioning

confidence: 99%

Stable isotopes of pedogenic carbonates as indicators of paleoecology in the Plio-Pleistocene (upper Bed I), western margin of the Olduvai Basin, Tanzania

Sikes

Ashley

2007

Journal of Human Evolution

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“…also contribute sediments to the Ganga Plain. The Sr and Nd isotope composition of the Himalayan and Peninsular rivers are quite distinct and is reflected by their bank sediments and river water (Krishnaswami et al, 1992;Jacobson and Blum, 2000;Bickle et al, 2001Bickle et al, , 2003Bickle et al, , 2005Dalai et al, 2003;Tripathi et al, 2004;Das et al, 2006;Rengarajan et al, 2009;Singh et al, 2008;Tripathy et al, 2010). A significant amount of Ganga Plain sediments are reworked by plainsfed river and again re-deposited within the Ganga Plain (Sinha and Friend, 1994;Gibling et al, 2005;Tripathi and Rajamani, 1999;Sinha et al, 2007; to sediments of the Bay of Bengal, information are scanty on erosion pattern of the Himalaya and their controlling factors from the Ganga Plain (Agrawal et al, 2013a, b).…”

Section: Climate Induced Temporal Change In Sr-nd Isotope Ratios In Tmentioning

confidence: 99%