Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A.

By J. Ohmann, Matthew J. Gregory. 2002

591 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/0c5ddaf9286875052ffac72b261a436009db060c

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Citations in the corpus

Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands (2012, methodology): “Studies such as Hudak et al. (2008) and Eskelson et al. (2009) have reported that MSN and RF produce more accurate estimates compared to other approaches such as gradient nearest neighbour (Ohmann and Gregory 2002), Euclidean distance and Mahalanobis distance.”
Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery (2009, methodology): “…al., 2001; McRoberts et al., 2007; Reese et al., 2003; Tokola et al., 1996; Tomppo, 1996; Tomppo & Halme, 2004), Mahalanobis distance, and metrics based on canonical correlation analysis (LeMay & Temesgen, 2005; Temesgen et al., 2003) and canonical correspondence analysis (Ohmann & Gregory, 2002).”
Old‐growth forests buffer climate‐sensitive bird populations from warming (2018, methodology): “…Pacific Northwestern United States (Figure 1); globally, this region is one of the few temperate forests with substantial remaining old growth (>20% of forest area; Davis et al., 2015; Thomas, Franklin, Gordon, & Johnson, 2006) and high- resolution data on its distribution (Ohmann & Gregory, 2002).”
Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data (2015, background): “Variations such as Most Similar Neighbor (MSN) (Moeur & Stage, 1995) and Gradient Nearest Neighbor (GNN) (Ohmann & Gregory, 2002) are simply k-NN using different distance metrics.”
Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects (2009, background): “Increasingly, these data spaces involve a larger suite of environmental variables used to describe landscapes (Goetz et al., 2009-this issue; Ohmann & Gregory, 2002).”
Partitioning Error Components for Accuracy-Assessment of Near-Neighbor Methods of Imputation (2007, methodology): “MSN distances are of the same form with W derived from analyses of canonical correlation (Moeur and Stage 1995), canonical regression (Stage and Crookston 2002), or of canonical correspondence (Ohmann and Gregory 2002).”
Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery (2007, methodology): “…Similar Neighbor (MSN) technique for which k=1 and the distance metric is based on canonical correlation (Hassani et al., 2004; Moeur & Stage, 1995) and the Gradient Nearest Neighbor (GNN) technique for which k=1 and the distance metric is based on canonical correspondence (Ohmann & Gregory, 2002).”
Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data (2008, methodology): “…techniques based on normalized and unnormalized Euclidean distance, Mahalanobis distance, Independent Component Analysis (ICA), Canonical Correlation Analysis (aka Most Similar Neighbor, or MSN), Canonical Correspondence Analysis (aka Gradient Nearest Neighbor, or GNN), and Random Forest (RF).”
The effects of habitat, climate, and Barred Owls on long-term demography of Northern Spotted Owls (2016, methodology): “…habitat map included stand age, canopy cover, average tree height, mean conifer diameter, conifer density, and forest type, and was derived from vegetation maps developed through a ‘‘gradient nearest neighbor’’ (GNN) analysis conducted for the entire Pacific Northwest (Ohmann and Gregory 2002).”
Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data (2011, methodology): “…optimized using observations of the response variables include the Canonical Correlation Analysis metric (LeMay & Temesgen, 2005; Moeur & Stage, 1995), the Canonical Correspondence metric (Ohmann & Gregory, 2002), and the Fuzzy, Multiple regression, and Nonparametric metrics (Chirici et al., 2008).”
Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion (2012, methodology): “Landscape heterogeneity of host availability for P. ramorum was characterized using geospatial vegetation data (summarized in Vaclavik et al., 2010) derived from gradient nearest neighbour (GNN) imputation at 30-m resolution (Ohmann & Gregory, 2002; Pierce et al., 2009; Ohmann et al., 2011).”
yaImpute: An R Package for kNN Imputation (2008, background): “Others include most similar neighbor (MSN, Moeur and Stage 1995) and gradient nearest neighbor (GNN, Ohmann and Gregory 2002).”