Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution

By R. Scheller, James B. Domingo, Brian R. Sturtevant, Jeremy S. Williams, A. Rudy, Eric J. Gustafson, David J. Mladenoff. 2007

498 citations recorded by the source corpus.

Subfields: Ecological Modeling, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d7a24922d98073f3f7cf09d3f457190e5df84362

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In Ecological Modeling

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Forest Growth and Yield Modeling (2011, methodology): “Although landscape-level models like LANDIS-II are available (Scheller et al., 2007), stand-level models are still useful for large-scale projections and offer some advantages.”
Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data (2009, background): “…tailored for these efforts, such as LANDIS and LANDIS II (Mladenoff & He, 1999; Scheller & Mladenoff, 2004; Schumacher et al., 2004; Sturtevant et al., 2004; Scheller et al., 2007), make use of pixel-level information to parameterize the land surface to the extent that these data are available.”
Extinction debt and colonization credit delay range shifts of eastern North American trees (2017, unknown): “LANDIS II 15 , FATE-HD 16 ) can easily capture local dynamics, they are inherently data demanding and computationally intensive 17,18 , making the estimation of entire distributions at continental scales challenging.”
Evolution and Prediction of Landscape Pattern and Habitat Quality Based on CA-Markov and InVEST Model in Hubei Section of Three Gorges Reservoir Area (TGRA) (2018, background): “Therefore, analyzing the spatial and temporal changes of landscape pattern in past, and exploring the impact of landscape pattern dynamics on biodiversity are beneficial to formulate land use management strategy scientifically and effectively, which can guide the sustainable development of regional ”
Big data for forecasting the impacts of global change on plant communities (2017, background): “…including the effects of global change (Thomas et al. , 2010; Zhu et al. , 2014; Serra-Diaz et al. , 2015), and to establish parameters and initial conditions for simulating community dynamics under global change scenarios (Schumacher et al. , 2006; Scheller et al. , 2007; Keane et al. , 2013).”