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Monday, April 9 • 11:00am - 11:15am
LAND USE/LAND COVER CHANGE: Mapping Dynamic Processes in Arid Rangelands

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AUTHORS: Ginger R.H. Allington*, George Washington University; Daniel G. Brown, University of Washington

ABSTRACT: Recent initiatives to assess changes in forest cover regionally and globally are clearly important, however they necessarily overlook significant portions of the global land area; regions dominated by non-forest vegetation. Rangelands, despite their importance for livelihoods of millions of people worldwide, have not received the same methodological or thematic attention. Here, we present a new ontology for grassland classification that characterizes spatio-temporal patterns to incorporate land-cover dynamics, land-use history, and grassland condition, based on known state-and-transition (STM) vegetation dynamics. Compared to traditional discrete classification schemes, our approach draws from research on state changes to classify and interpret temporal signals of a simple vegetation index (NDVI) derived from moderate-resolution (30m) remote sensing data from Landsat sensors. We present a revised classification system based on five categories of linear and non-linear change in productivity over time, which represent distinct vegetation trajectories. We found that, while traditional land cover datasets primarily classified our case study regions as having stable classes over the timeseries, we identified many areas that experienced significant negative and non-linear trends in productivity. We also explored how our proposed classes manifest in detectable landscape patterns within our case study regions. We found that the positive trend classes tend to exhibit more regular shapes (likely due to the influence of the regular shapes of agricultural fields), while the negative trends exhibit more irregular shapes but are more aggregated at the landscape scale.Future work to quantify the global status of rangeland resources and degradation hinges on innovation in strategies for classification that encompass the dynamic nature of these systems. Our work builds on previous work on detecting trends in greenness by identifying timeseries signatures & linking them to known vegetation transitions, even in areas without change in dominant cover type.

Monday April 9, 2018 11:00am - 11:15am CDT
Grant Park Parlor