Spatial‐temporal monitoring of forest disease dynamics

Liu et al. 2006. Remote Sensing of Environment. Traditional mapping approaches using per-pixel, single-date image classifications have not generated consistently satisfactory results. Incorporation of spatial–temporal contextual information can improve these results. In this paper, we propose a spatial–temporally explicit algorithm to classify individual images using the spectral and spatial–temporal information derived from multiple co-registered images.

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Support vector machines for predicting distribution of Sudden Oak Death in California

Guo, et al. 2005. Ecological Modeling. Predicted area of SOD risk in northern CaliforniaWe present an alternative method to conventional environmental niche modeling approaches by developing support vector machines (SVMs), which are the new generation of machine learning algorithms used to find optimal separability between classes within datasets, to predict the potential distribution of Sudden Oak Death in California.

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Landscape dynamics of the spread of Sudden Oak Death

Kelly and Meentemeyer. 2002. Photogrammetric Engineering and Remote Sensing. We present a landscape-scale We present a landscape-scale study of the spatio-temporal dynamics of oak mortality using second-order spatial point-pattern analysis techniques and classification trees.

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Mapping seagrass bed pattern

Kelly, N. M., M. Fonseca, and P. Whitfield. 2001. Aquatic Conservation: Marine and Freshwater Ecosystems. An empirically derived logistic multiple regression model and a Boolean logic suitability model were used to produce several predictive map products, including: susceptibility of seagrasses to storms, probability of seagrass cover, and suitability of areas for restoration of seagrassesin Core Sound, North Carolina, USA.

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