The Natural Soil Drainage Index: An Ordinal Estimate of Long-Term Soil Wetness

Schaetzl, Randall J.; Krist, Frank J.; Stanley, Kristine; Hupy, Christina M.

doi:10.2747/0272-3646.30.5.383

Cited by 33 publications

(15 citation statements)

References 48 publications

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“…The motivation for deriving a soil drainage index is that we are particularly interested in understanding its spatial distribution across the landscape, as this is probably more useful from a land management and assessment perspective. Studies such as Schaetzl et al (2009) demonstrate this. Furthermore, after a number of years surveying the area described in this study, we have developed a mental concept of how soil drainage varies across the landscape.…”

Section: Introductionmentioning

confidence: 83%

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia

Malone

McBratney

Minasny

2018

PeerJ

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Soil colour is often used as a general purpose indicator of internal soil drainage. In this study we developed a necessarily simple model of soil drainage which combines the tacit knowledge of the soil surveyor with observed matrix soil colour descriptions. From built up knowledge of the soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining soils generally follows the colour sequence of red → brown → yellow → grey → black soil matrix colours. For each soil profile, soil drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference soil colours of the soil drainage colour sequence. The estimation of drainage index at each profile incorporates the whole-profile descriptions of soil colour where necessary, and is weighted such that observation of soil colour at depth and/or dominantly observed horizons are given more preference than observations near the soil surface. The soil drainage index, by definition disregards surficial soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of soil drainage we digitally mapped the index across our study area. Spatial inference of the drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson’s correlation coefficients indicated the variables most strongly correlated with soil drainage were topographic wetness index (−0.34), mid-slope position (−0.29), multi-resolution valley bottom flatness index (−0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of soil drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 soil drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of soil colour description, these results are acceptable. Furthermore, the spatial distribution of soil drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique soil colour and soil drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged.

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Section: Introductionmentioning

confidence: 83%

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia

Malone

McBratney

Minasny

2018

PeerJ

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“…This includes soil, landform, temperature, moisture, and nutrient availability, as all are important factors contributing to native plant community development. (Schaetzl et al 2009) and productivity (Schaetzl et al 2012) for the lower 48 states at a 250m resolution were also assessed for use with the imputation modelling.…”

Section: Data and Pre-analysismentioning

confidence: 99%

Imputing plant community classification from associated forest inventory and physiographic data in Minnesota, USA

Wilson

2017

Ecological Indicators

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We use conditions defining growing space (e.g., sum of all relevant biotic and abiotic factors) to systematically associate forested stands with likely native plant community (NPC) classifications. We develop and test imputation methodology to link ecological information with forest inventory records, i.e., to individual stands. These efforts will reduce the cost of obtaining needed ecological data with the detail and site specificity required for regional landscape-scale management. We employ associative techniques similar to those used by the Minnesota Department of Natural Resources native plant sampling program to group sites sharing multiple conditions and plant species. However, the focus here is on linking plant community observations with conditions observed during routine forest stand inventory efforts. Unique associations of herbaceous plants are thought to occur in conjunction with different sets of trees on sites with appropriate soils, physiography, moisture, and disturbance histories. Following this hypothesis, NPC assignments based on field observation of herbaceous and woody plants are used as a source of training data to identify and categorize stands already characterized by routine forest inventory efforts. Additional soils, climate, and physiographic data have also been associated with NPC and forest inventory observations to provide information about shared characteristics influencing vegetation on a site. Imputed classifications provide 4 levels of information about ecological conditions on a stand; ecological system, floristic region, and moisture and nutrient availability. Results suggest that with 13 ecological systems, we might expect an average of 70% ± 10% of imputations to be correct at the system level. Imputation of floristic region, moisture index, and nutrient index ratings resulted in roughly 90% accuracy for each component. The compounding of small error rates in components of the NPC class code leads to a 47% ± 10% rate of success over failure in imputation of the class a stand might later be assigned based on relevé data. Relative correspondence of imputed classes to those assigned through observation (e.g., similarity to the true classification; precision) is roughly 87%. Essentially, this means that even when incorrect, imputed classes are very close to those assigned based on observation of physical and vegetative associations. This level of success will help bioregional landscape management efforts through savings in NPC mapping and classification efforts, and the ecological understanding provided.

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“…For a few counties in the eastern U.S. and several places in the western U.S., gSSURGO spatial data were not available, and we substituted data from the polygon-based State Survey Geographic Dataset (STATSGO2; Soil Survey Staff 2017b). We also included landscape Productivity Index and Drainage Index as measures of landscape-scale soil properties of each site (Schaetzl et al 2009(Schaetzl et al , 2012. We used average recent historical climate data from the Multivariate Adapted Constructed Analogs (MACA) v2 METDATA data set (Abatzoglou 2013) to derive 19 bioclimatic variables (Hijmans et al 2005), as well as annual averages of potential evapotranspiration and solar radiation.…”

Section: Predicting the Classifications Using Environmental Variablesmentioning

confidence: 99%

Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches

et al. 2018

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Background: Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) plots. Methods: Each plot had three forest community type labels: (1) "FIA" types were assigned by the FIA program using a supervised method; (2) "USNVC" types were assigned via a key based on the U.S. National Vegetation Classification; (3) "empirical" types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables.

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The Natural Soil Drainage Index: An Ordinal Estimate of Long-Term Soil Wetness

Cited by 33 publications

References 48 publications

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia

Imputing plant community classification from associated forest inventory and physiographic data in Minnesota, USA

Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches

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