Digital terrain modelling: A review of hydrological, geomorphological, and biological applications

By I. Moore, R. Grayson, A. Ladson. 1991

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Citations in the corpus

Using topographic wetness index in vegetation ecology: does the algorithm matter? (2010, methodology): “1991) but now topographic indexes derived from digital elevation models (DEMs) within geographic information system (GIS) environments are used (Moore et al. 1991; Iverson et al. 1997; Gruber & Peckham 2008).”
Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran (2015, methodology): “The topographic wetness index (TWI) is a secondary topographic factor within the runoff model which is clarified according to (Moore et al. 1991): TWI ¼ ln β tanα ð1Þ where, ß is the cumulative upslope area draining through a point (per unit contour length). tan α is the slope angle at the point.”
A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland (2008, methodology): “Environmental variables used in the statistical analyses and their description (Goodrich, 1982; Clark et al., 1985; Moore et al., 1991) Environmental variable Description Source Mean altitude (m) Temperature, windiness Digital elevation model Mean slope angle (°) Potential energy, soil thickness…”
Ensemble-based landslide susceptibility maps in Jinbu area, Korea (2012, methodology): “Topographic indices have, therefore, been used to describe spatial soil moisture patterns (Moore et al. 1991).”
An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling (2006, background): “Various hydrologic parameters, such as surface slope, aspect (Srinivasan and Engel 1991), surface curvature (Moore et al. 1991), and terrain wetness index (Chirico et al. 2003), can be derived from the raster-based DEMs.”
Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors (2009, methodology): “Terrain-distributed solar radiation (Dubayah and Rich 1995) was modeled from U.S. Geological Survey 30-m resolution digital elevation model (DEMs) using the Solar Analyst 1.0 extension for ArcView (ESRI, Redlands, CA, USA) Geographic Information System (GIS).”
Remote sensing for wetland classification: a comprehensive review (2018, methodology): “For example, the Topographic Position Index (Weiss 2001), Compound Topographic Index (Moore, Grayson, and Ladson (1991)), slope, and orientation of the DEM have been used in several studies for wetland classification (Hogg and Todd 2007; Murphy et al. 2007; Smith, Macleod, and Kloiber 2012; Rampi,…”
Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances (2013, methodology): “These conditioning factors were derived based on slope map and specific catchment area (AS) (Moore and Burch 1986; Moore et al. 1991).”
Valuing watershed services: concepts and empirics from southeast Asia (2004, methodology): “(1) (Moore et al., 1991; Lal, 1993).”
Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016, background): “Predictor variables included transformed aspect, slope, elevation, topographic positional index (Moore et al. 1991), Bailey's ecoregions (Bailey 1995), land cover and tree canopy cover from the 2001 NLCD (Homer et al. 2004), and Landsat-5 reflectance bands (Table 3).”