Abstract

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.

Highlights

  • Increasing spread of invasive vegetation, such as cheatgrass (Bromus tectorum L.), can contribute to ecosystem degradation and increased disturbance frequency within dryland ecosystems that cover roughly40% of the Earth’s land surface [1,2,3]

  • Remote sensing of exotic annual grasses in dryland ecosystems of the western United States has largely relied on repeat, multispectral imagery and phenological difference techniques; where some exotic annual grasses are characterized by early spring green-up, senescence, and amplified inter-annual growth response to precipitation that is distinct from most native species [24,25,26]

  • The overarching goal of this research was to develop a fully automated and scalable system that leverages in situ observations, harmonized Landsat and Sentinel-2 (HLS) data and derived metrics, and data mining techniques for mapping exotic annual grass (%) cover (30-m spatial resolution) throughout dryland ecosystems in the western United States

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Summary

Introduction

Increasing spread of invasive vegetation, such as cheatgrass (Bromus tectorum L.), can contribute to ecosystem degradation and increased disturbance frequency within dryland ecosystems that cover roughly40% of the Earth’s land surface [1,2,3]. Remote sensing of exotic annual grasses in dryland ecosystems of the western United States has largely relied on repeat, multispectral imagery and phenological difference techniques; where some exotic annual grasses are characterized by early spring green-up, senescence, and amplified inter-annual growth response to precipitation that is distinct from most native species [24,25,26]. Distribution models and maps of exotic annual grass cover support early detection and rapid response initiatives if data inputs have high spectral, spatial, and temporal resolution (or if proliferation is distinct, homogenous, or slow growing), but few studies have made use of technologies that provide accurate and spatially-detailed information at latencies requested by land resource managers

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