Riparian Vegetation Change Along the Snake River in the Fort Hall Area: A 2016–2024 Analysis and 2032 Forecast
Faculty Mentor Information
Keith Weber, Idaho State University
Presentation Date
7-15-2026
Abstract
Riparian vegetation along the Snake River in the Fort Hall area is vital for water filtration, preservation of local biodiversity, and stream bank stabilization. This study showcases how riparian land cover has changed between 2016 and 2024 and projects the future landscape of 2032 based on current trends and future transition potentials. This analysis was completed using remote sensing data and geospatial modeling. LANDFIRE Existing Vegetation Type (EVT) data from 2016 and 2024 were processed in ArcGIS Pro and IDRISI TerrSet. The Land Change Modeler (LCM) in TerrSet was used to analyze land cover transitions and generate a CA-Markov chain prediction for 2032, which uses LiDAR-derived elevation, slope, aspect, and distance to surface water as driver variables along with land cover/land use transition potentials. Multidimensional Normalized Difference Water Index (NDWI) values derived from five Landsat scenes 2016, 2017, 2020, 2022, and 2024 were examined using a Mann-Kendall spatial trend analysis to identify areas where statistically significant changes in vegetation moisture exist. By 2032, 5,351 acres of riparian land are projected to change to development, and 2,675 acres to open water. These findings are intended to support water and land management decisions on or near Fort Hall Indian Reservation.
Riparian Vegetation Change Along the Snake River in the Fort Hall Area: A 2016–2024 Analysis and 2032 Forecast
Riparian vegetation along the Snake River in the Fort Hall area is vital for water filtration, preservation of local biodiversity, and stream bank stabilization. This study showcases how riparian land cover has changed between 2016 and 2024 and projects the future landscape of 2032 based on current trends and future transition potentials. This analysis was completed using remote sensing data and geospatial modeling. LANDFIRE Existing Vegetation Type (EVT) data from 2016 and 2024 were processed in ArcGIS Pro and IDRISI TerrSet. The Land Change Modeler (LCM) in TerrSet was used to analyze land cover transitions and generate a CA-Markov chain prediction for 2032, which uses LiDAR-derived elevation, slope, aspect, and distance to surface water as driver variables along with land cover/land use transition potentials. Multidimensional Normalized Difference Water Index (NDWI) values derived from five Landsat scenes 2016, 2017, 2020, 2022, and 2024 were examined using a Mann-Kendall spatial trend analysis to identify areas where statistically significant changes in vegetation moisture exist. By 2032, 5,351 acres of riparian land are projected to change to development, and 2,675 acres to open water. These findings are intended to support water and land management decisions on or near Fort Hall Indian Reservation.