Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods

By Gretchen G. Moisen, Elizabeth A. Freeman, J. Blackard, T. Frescino, N. E. Zimmermann, THOMAS C. EDWARDS JR.. 2006

299 citations recorded by the source corpus.

Subfields: Ecological Modeling, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/df7b487ab5b10fe6f887f155cdf1fe13513de38f

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In Ecological Modeling

In Environmental Engineering

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Citations in the corpus

GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, background): “These errors may be affecting the overall accuracies of the models (Moisen et al. 2006).”
Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery (2017, methodology): “While the general approach employed by Moisen et al. (2006) is similar to that described here (multi-temporal Landsat imagery), our range ofmodel fit is higher, indicating that the additional image processing techniques and spectral unmixing approach employed here may improve abundance mapping…”
Effects of sample survey design on the accuracy of classification tree models in species distribution models (2006, methodology): “Discussion redictive statistical models are commonly used tools to estiate the likelihood of species presence (Edwards et al., 1996; McNoleg, 1996; Frescino et al., 2001; Lawler and Edwards, 2002; Moisen et al., 2006).”
Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota (2011, background): “However, improvements in the level of detail and precision among many estimated parameters continue to be a challenge (Couturier et al., 2009; Moisen et al., 2006; Wolter et al., 1995, 2008, 2009), especially when the aim is to augment or supplant traditional, ground-based forest inventory data…”
WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS? (2007, background): “BRT and MAXENT are new in ecological modeling, with few ecological applications available (e.g., Kawakita et al. 2005, Leathwick et al. 2006a, Moisen et al. 2006, Phillips et al. 2006).”
Species distribution models and ecological theory: A critical assessment and some possible new approaches (2007, methodology): “Recent applications of new statistical procedures for modelling (Moisen et al., 2006; Munoz and Felicisimo, 2004; Elith et al., 2006) expand the potential for examining the complex curves and interactions that may be postulated by ecological theory (Austin and Smith, 1989) or detected by…”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “…credit risk assessment [125], mobility prediction [126], anti-money laundering [127], optimisation of transduction [128], strength prediction [129], travel time prediction [130], modelling energy consumption [131], geography [132, 133], medical diagnosis [134, 135, 136], image recognition [137].”
Ecology of butterflies in Europe (2009, background): “A number of studies comparing different modelling techniques could not identify any single method to be superior for all species and situations (Guisan et al., 1999; Manel et al., 1999; Moisen & Frescino, 2002; Olden & Jackson, 2002; Segurado & Araújo, 2004; Brenning, 2005; Moisen et al., 2006; Pott”
Dispersal, disturbance and the contrasting biogeographies of New Zealand’s diadromous and non‐diadromous fish species (2008, background): “While the strong performance of boosted methods has been known for a number of years, we are aware of only a few instances in which they have been applied to the analysis of ecological data (Cappo et al., 2005; Kawakita et al., 2005; Leathwick et al., 2006; Moisen et al., 2006).”
Comparing the Performance of Statistical Models for Predicting PM10 Concentrations (2014, background): “BRT is one of them and is explored and applied mainly by ecologists (Cappo et al., 2005; Moisen et al., 2006; De’ath, 2007; Elith et al., 2008).”
PresenceAbsence: An R Package for Presence Absence Analysis (2008, background): “It is the validation dataset from Moisen et al. (2006), and more detailed information can be found in this paper.”