The Intergovernmental Panel on Climate Change (IPCC) uses scenario development as its major vehicle for visualizing potential future conditions, their consequences, and adaptation options. Unfortunately, initial efforts to downscale IPCC socioeconomic scenarios to levels relevant for policy have not adequately represented land cover and land use change (LCLUC) in mountain landscapes. Exurban development, the fastest growing land use type in these landscapes, is either not resolved or projected accurately. Modeling of rural LCLUC can be improved through new remote-sensing techniques for detecting the fine-grained development typical in mountains and better understanding of the context-dependency of drivers of LCLUC. Our goal is to project LCLUC under IPCC. scenarios across western mountain landscapes and to apply the results to enhance vulnerability assessments of biodiversity to future global change. Our objec tives are to: 1. Enhance USGS efforts to quantify LCLUC in landscape samples for the period 1973-2014 through innovative remote sensing applications 2. Test hypotheses on the relative and context-dependent influence on rural LCLUC of proximity to cities and markets, natural resources, natural amenities, and climate change 3. Modify the SERGoM land use change model based on the results of the hypothesis testing and project LCLUC under the IPCC scenarios 4. Assess the influence of projected LCLUC on wildlands and tree species habitat suitability.
The project focuses on the Rocky Mountain, Cascade, and Coast Ranges in the northwestern U.S., which vary in socioeconomic status of communities and in rates of past climate change. The project will leverage the newly completed USGS Land Trends Project that quantified LCLUC during 1973-2000 in samples across the U.S. For a subset of these samples stratified by community type and past climate change, we will improve the data set by dividing the developed class to into exurban, suburban, and urban classes refining the disturbed class to distinguish abrupt and chronic events and extending the analysis to 2014. Methods include processing of high-resolution optical imagery and high-frequency analysis of the Landsat archive using LandTrendr and Vegetation Change Tracker and acquisition of predictor data at the parcel level. These data will be used to test hypotheses on the context-dependency of LULUC with spatial econometric modeling of parcel, neighborhood, and county level drivers. We hypothesize that the effects of traditional natural resources vs natural amenities varies with socioeconomic status of community. Similarly, climate warming may inhibit exurban development in hot/dry areas but favor exurban development in areas with harsh winters. Knowledge of context-dependency is critical to future projection because directions of LCLUC may shift as socioeconomic or climate thresholds are reached. Results will be used to modify how the SERGoM LULUC model downscales demand under IPCC scenarios and projects LCLUC by decade to 2100. Projected climate and land use will be used in a vulnerability assessment of wildlands and tree species habitat suitability.
The proposed work is highly responsive to the NRA in advancing the state of the art remote sensing for quantifying LCLUC in mountain landscapes, integrating economic and ecological theory to improve current knowledge on the context - dependency of drivers of LCLUC, developing a protocol for quantifying climate change impacts on land use that are expected to become more frequent as climate change continues to be expressed and providing a model for downscaling land use change under the IPCC scenarios that can be applied to mountains. Moreover, the proje ct will develop methods that will enhance our planned expansion to international applications with the IUCN.