A plant ecology approach to digital soil mapping, improving the prediction of soil organic carbon content in alpine grasslands

Ballabio, Cristiano; Fava, Francesco; Rosenmund, Alexandra Stella

doi:10.1016/j.geoderma.2012.04.002

Cited by 34 publications

(18 citation statements)

References 46 publications

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“…The lack of stronger relationships of soil properties to vegetation was most likely a function of the relative homogeneity of the vegetation assemblage in the observed catchment and the relatively small catchment area, 10 0 ha, encompassed by this study. These results contrast with those of previous DSM studies at larger spatial resolutions spanning areas on the order of 10 4 to 10 6 ha that identified strong relationships between soil variation and remotely sensed vegetation indices (Ballabio et al, 2012;Dobos, 2003).…”

Section: Factors Controlling Soil Property Spatial Variationcontrasting

confidence: 99%

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

Holleran

Levi

Rasmussen

2015

SOIL

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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.

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Section: Factors Controlling Soil Property Spatial Variationcontrasting

confidence: 99%

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

Holleran

Levi

Rasmussen

2015

SOIL

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“…In natural ecosystems, vegetation coexists with soil as part of a feedback system, where vegetation 538 influences soil development, and in turn, the distribution of plant communities is influenced by soil 539 physical and chemical properties (Ballabio et al, 2012). Within a landscape, the spatial distribution of 540 soils and topography can create unique soil-climatic environments where only plant communities 541 adapted to those conditions (i.e., ecological niche) can survive.…”

Section: Soil-climate-vegetation Relationships 537mentioning

confidence: 99%

“…655 While few studies have developed soil proxies from vegetation cover, one approach has been to 656 associate soil properties with plant functional types (PFT), which represent broad groupings of 657 vegetation based on similarities in morphological and physiological traits constrained by environmental 658 resources and conditions (Ustin and Gamon, 2010). Prior efforts to map PFT using remote sensing have 659 employed a static or mono-temporal approach (Buis et al, 2009; Schaepman et al, 2007; Ustin and 660Gamon, 2010), and while these efforts are useful in characterizing soil-vegetation spatial variation 661(Ballabio et al, 2012), growing evidence supports the notion that temporal variability in vegetation 662 spectra, as driven by soil and climate feedbacks, is an important predictor of soil variability. Since 663 environmental resources and conditions change in response to internal and external drivers, the 664 morphological and physiological traits expressed by PFTs will also change.…”

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confidence: 99%

Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability

Maynard

Levi

2017

Geoderma

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“…Those demonstrably soil forming (and therefore soil characterizing) parameters include vegetation (e.g. Ballabio, Fava, & Rosenmund, ), topography (Bodaghabadi et al., ; ten Caten, Diniz Dalmolin, Pedron, & Mendonca‐Santos, ), geology (Chagas, de Carvalho, & Bhering, ) and climate (Fujii et al., ). Standard modern soil survey datasets usually include a number of different parameters, to satisfy a wide range of (often as‐yet‐unanticipated) modelling and mapping requirements.…”

Section: Introductionmentioning

confidence: 99%

Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling—Part 1: Mapping of soil classes

Coull

2019

Soil Use and Management

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A digital mapping approach was applied to soils in Scotland, producing maps at 100-m resolution and different levels of classification. This used neural networks to predict fuzzy soil class weightings based upon site descriptors from existing soil survey data. The intention of this work was to produce a set of soil maps for Scotland, which provide greater spatial resolution mapping than currently available, and provide fuzzy data to be used in mapping of ecosystem services using a novel approach explained in a second paper. When selecting the class with the highest weighting for test data points, Producer's Accuracy of 70.7% was achieved in mapping the broadest classification (five classes), while Producer's Accuracy levels of 59.9% and 43.7%were achieved for 11 and 30 classes, respectively. Evaluation of confusion matrices generated from the neural network model tests showed that, particularly for the classification systems with 11 and 30 classes, misclassification errors were more common between similar soil classes than between classes that were very different from one another. This implies that straightforward estimation of classification accuracy that measures "correct" and "incorrect" gives misleading results, and that the fuzzy classification maps are more useful than the crisp classification accuracy values.These fuzzy classification maps are therefore potentially useful for evaluating ecosystem services relating to soil type. We conclude that the mapping approach used here provides a classification accuracy comparable to other approaches and that the outputs can be used for mapping soil properties, processes, functions and ecosystem services. K E Y W O R D Sdigital soil mapping, land management, remote sensing, soil classification, spatial resolution 206 |

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A plant ecology approach to digital soil mapping, improving the prediction of soil organic carbon content in alpine grasslands

Cited by 34 publications

References 46 publications

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

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

Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability

Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling—Part 1: Mapping of soil classes

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