Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest)

By Rick L. Lawrence, S. Wood, R. Sheley. 2006

726 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4a75143bab23e2174df1ec9409a54fcbba3c19a0

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Citations in the corpus

Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with Random Forest (2014, methodology): “The sum of the diagonals of the grey level co-occurrence matrix; used features (using the mean of all layers): GLDV Angular second moment. species (Lawrence, Wood, and Sheley 2006).”
Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods (2015, background): “Furthermore, the OOB error is calculated thousands of times (based on the ntree value), each time with different random partitioning of the data which was then averaged to calculate the overall error rate (Liaw and Wiener 2002; Lawrence et al. 2006).”
Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery (2008, methodology): “While Random Forest, a tree-based ensemble classifier, has been shown effective when applied to classification of hyperspectral data (Crawford et al., 2003; Ham et al., 2005; Lawrence et al., 2006), there has not been adequate discussion on Adaboost.”
Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data (2013, methodology): “That demonstrated the utility of RF as a feature-selection method (Lawrence, Wood, and Sheley 2006).”
Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016, result): “Lawrence et al. (2006) found that RF outperformed CART models for mapping invasive plants using hyperspectral imagery and also found that the out-of-bag (OOB) accuracy assessments provided by RF were reliable when compared with withheld test data.”
Object-oriented mapping of landslides using Random Forests (2011, background): “Since the fundamental works on ensemble decision trees (e.g. Breiman, 2001), Random Forests (RF) have already provided promising results in fields such as genomics (Diaz-Uriarte & Alvarez de Andres, 2006), ecology (Cutler et al., 2007) and remote sensing (Lawrence et al., 2006; Watts et al., 2009).”
Habitat suitability modelling of an invasive plant with advanced remote sensing data (2009, background): “…hyperspectral image data (e.g. Parker Williams & Hunt, 2002; Underwood et al., 2003; Asner & Vitousek, 2005; Lass et al., 2005; Mundt et al., 2005; Lawrence et al., 2006; Cheng et al., 2007; Hamada et al., 2007; Hestir et al., 2008; Noujdina & Ustin, 2008) for mapping the distribution of…”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “Generally, RF has been shown to be robust to complex feature spaces (Guyon and Elissee ﬀ 2003; Lawrence, Wood, and Sheley 2006; Duro, Franklin, and Dubé 2012b).”
Potential of remote sensing to predict species invasions (2015, methodology): “Together with these algorithms, more recent techniques have been developed based on decision trees (e.g. random forest; Lawrence et al., 2006), or object-oriented approaches (Bunting and Lucas, 2006; Plaza et al.”
Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis (2018, methodology): “all three study areas, the ntree parameter was set up at 1000, because previous studies proved that there is no increase in the number of errors beyond the creation of 1000 classification trees from randomly selected samples (Lawrence et al., 2006).”
Modeling Species Distribution and Change Using Random Forest (2011, background): “This algorithm is gaining prominence in remote sensing (Lawrence et al. 2006), forestry (Falkowski et al.”