Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils

Häring, Tim; Dietz, Elke; Osenstetter, Sebastian; Koschitzki, Thomas; Schröder, Boris

doi:10.1016/j.geoderma.2012.04.001

Cited by 97 publications

(46 citation statements)

References 55 publications

(70 reference statements)

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“…relationship between them. Nevertheless, this is an elementary process under conditions of multiple acting variables (Häring et al, 2012), such as cumulative human impacts. For this reason, partial dependence plots (Friedman, 2001;Friedman et al, 2009) can be used to depict the relationship of alien species presence probability on each predictor after averaging out the effects of all classification predictors (Cutler et al, 2007).…”

Section: Evaluation Criteriamentioning

confidence: 99%

Spatial Downscaling of Alien Species Presences Using Machine Learning

2017

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Spatially explicit assessments of alien species environmental and socio-economic impacts, and subsequent management interventions for their mitigation, require large scale, high-resolution data on species presence distribution. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A bootstrapping scheme is designed to account for sub-setting uncertainty, and subsets are used to train a sufficiently large number of RF models. RF results are processed to estimate variable importance and model performance. The method is tested with an ∼8 × 8 km 2 grid containing floral alien species presence and several potentially exploratory indices of climatic, habitat, land use, and soil property covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16 × 16 km 2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Uncertainty assessment of the spatial downscaling of alien species' occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution. Furthermore, the RF architecture allows for tuning toward operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

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Section: Evaluation Criteriamentioning

confidence: 99%

Spatial Downscaling of Alien Species Presences Using Machine Learning

2017

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“…Data gaps were filled with the help of digital soil mapping. Complex soil units were split according to relief position [17]. Thus, a finer scale of the map was derived.…”

Section: National Forest Inventory Datamentioning

confidence: 99%

Possibilities and Limitations of Spatially Explicit Site Index Modelling for Spruce Based on National Forest Inventory Data and Digital Maps of Soil and Climate in Bavaria (SE Germany)

Brandl

Falk

Klemmt

et al. 2014

Forests

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Combining national forest inventory (NFI) data with digital site maps of high resolution enables spatially explicit predictions of site productivity. The aim of this study is to explore the possibilities and limitations of this database to analyze the environmental dependency of height-growth of Norway spruce and to predict site index (SI) on a scale that is relevant for local forest management. The study region is the German federal state of Bavaria. The exploratory methods comprise significance tests and hypervolume-analysis. SI is modeled with a Generalized Additive Model (GAM). In a second step the residuals are modeled using Boosted Regression Trees (BRT). The interaction between temperature regime and water supply strongly determined height growth. At sites with very similar temperature regime and water supply, greater heights were reached if the depth gradient of base saturation was favorable. Statistical model criteria (Double Penalty Selection, AIC) preferred composite variables for water supply OPEN ACCESSForests 2014, 5 2627 and the supply of basic cations. The ability to predict SI on a local scale was limited due to the difficulty to integrate soil variables into the model.

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“…Because of the clear advantages, several authors applied tree-based methods for soil mapping problems (Hengl, T. et al 2007;Grinand, C. et al 2008;Cambule, A.H. et al 2013;Sun, X.L. et al 2011;Häring, T. et al 2012;Pásztor, L. et al 2013). Complex similarity (relatedness) or dissimilarity (distance) matrices and their analysis in tree form are routine procedures in several disciplines such as in psychology (Pecora, L.M.…”

Section: Resultsmentioning

confidence: 99%