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- W1640996891 abstract "Methods (Fig. 2) • Remote Sensing Analysis (Objective 1) – Multiple Endmember Spectral Mixture Analysis (MESMA; Roberts et al. 1998) • Implemented in ERDAS IMAGINE, ENVI, and ArcGRID software • Derive sub-pixel abundances of surface materials for each year of imagery – Change detection using fuzzy logic (Zadeh 1965) • Implemented in ArcGIS and IDRISI software • Quantify temporal changes in the abundance of surface materials • Spatial Modeling (Objectives 2 and 3) – Local Indicator of Spatial Association (LISA, Local Moran’s I Statistic; Anselin 1995; Fig. 3) • Determine whether temporal abundance changes of woody plants are spatially random or not – Conceptual Model of WPE (Fig. 4) – Geographically Weighted Regression (GWR; Brundson et al. 1996), Weights of Evidence (WoE; Bonham-Carter et al. 1988), and Weighted Logistic Regression (WLR; Agterberg et al. 1993) • Derive weights or regression coefficients of explanatory variables • Predict probability of WPE across study area • Implemented in GWR, ArcSDM, and IDRISI software QUANTIFYING THE SPATIO-TEMPORAL DYNAMICS OF WOODY PLANT ENCROACHMENT: AN INTEGRATIVE REMOTE SENSING, GIS, AND SPATIAL MODELING APPROACH" @default.
- W1640996891 created "2016-06-24" @default.
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- W1640996891 date "2007-01-01" @default.
- W1640996891 modified "2023-09-24" @default.
- W1640996891 title "Quantifying the spatio-temporal dynamics of woody plant encroachment using an integrative remote sensing, GIS, and spatial modeling approach." @default.
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