Development of Spatially Explicit State-and-Transition Model Maps for Rangeland Management
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Authors
Phipps, Lucas A.
Issue Date
2024
Type
Dissertation
Language
Keywords
Ecological Sites , Monitoring , Random Forest , Rangeland , Soil Mapping , Vegetation Mapping
Alternative Title
Abstract
Assessing vegetation conditions in expansive rangeland ecosystems has long posed a persistent challenge. Recent advancements in remote sensing technologies have provided new tools for improving this assessment process. In this study, conducted at two ranch sites in Nevada, we integrated existing line-point-intercept monitoring data with additional field observations to evaluate the vegetation condition or "State" at various monitoring points. We then employed machine learning techniques to classify gridded raster datasets, aligning them with existing State-and-Transition models (STMs) specific to each study area. Leveraging Landsat-derived fractional cover datasets, as well as climate and soil predictors, we aimed to predict vegetation State in specific Vegetation Great Groups (VGGs). The resulting vegetation State maps were then combined to generate a cohesive representation of vegetation conditions across the study sites. Our analysis revealed that relative functional group cover emerged as a superior predictor of vegetation state and ecological processes at the site level. However, we encountered variations in state mapping accuracy ranging from approximately 14% to 44% error. These discrepancies were influenced by factors such as study location, landscape heterogeneity, availability of training data, and species-specific challenges, all of which complicate the accurate classification of remote sensing datasets.