Mapping and Monitoring Cheatgrass Dieoff in Rangelands of the Northern Great Basin, USA

Boyte, Stephen P.; Wylie, Bruce K.; Major, Donald J.

doi:10.1016/j.rama.2014.12.005

Cited by 31 publications

(25 citation statements)

References 39 publications

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“…For example, the National Land Cover Database (NLCD), which is derived from Landsat data, has been used in combination with EROS Moderate Resolution Imaging Spectroradiometer (eMODIS) vegetation products (Jenkerson et al 2010) to create a cheatgrass index based on phenology ( Fig. 12.4; Boyte et al 2015). Climate variable models such as Daymet (Thornton et al 2018) that use DEMs created from Shuttle Radar Topography Mission (SRTM) data have been used in combination with eMODIS vegetation products to monitor the spread of cheatgrass (Downs et al 2016).…”

Section: Rangelands and Grasslandsmentioning

confidence: 99%

Remote Detection of Invasive Alien Species

Bolch

Santos

Ade

et al. 2020

Remote Sensing of Plant Biodiversity

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Human-mediated IAS introductions, deliberate or unintentional, tend to be much faster than natural processes (e.g., wind, animal; Theoharides and Dukes 2007; Hulme 2009; Pyšek et al. 2009; Seebens et al. 2017). Invasion pathways differ between taxa; intentional transport (escape and release) is most important for plants and vertebrates, while unintentional transport is more significant for invertebrates, algae, and microorganisms (Saul et al. 2017). Roads, tracks, and waterways create natural and artificial corridors for invasion, exposing ecosystems to invasion, particularly in emerging economies where development is rapid (Mortensen et al. 2009; Masters and Norgrove 2010). Globally, the continued expansion of tourism, air transport, and trade is dramatically heightening propagule pressure and subsequent invasion (Hulme 2015). Global environmental changes, particularly changes in climate and weather patterns, nutrient cycles, and land use, generally drive increasing invasions while also making invasion prevalence, impacts, and feedbacks to the Earth system less predictable (Bradley et al. 2010; Dukes and Mooney 1999). These same change processes can also alter IAS transport and introduction mechanisms, hindering monitoring and control (Hellmann et al. 2008; Walther et al. 2009) and making it more challenging to predict future spread. Moreover, these changes stress ecosystems and increase invasion success (Simberloff 2000). Climate and land use changes drive species range shifts, potentially creating new invasion hotspots (Bellard et al. 2013; Bradley et al. 2010) while decreasing invasion risk and increasing recovery potential in other regions (Allen and Bradley 2016). Thus, observing the geographic patterns of the spread of IAS is critical to understand their origins, pathways, and invasion processes on a changing planet.

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Section: Rangelands and Grasslandsmentioning

confidence: 99%

Remote Detection of Invasive Alien Species

Bolch

Santos

Ade

et al. 2020

Remote Sensing of Plant Biodiversity

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“…A modelling technique for ecosystem performance using a rule-based piecewise regression approach was originally developed for a boreal forest ecosystem [29]. Since then, the approach has been used in multiple other studies focusing on grassland ecosystems [51,57,58]. The technique provides precise modelling of complex systems and produces an outcome that enhances the understanding of relationships between dependent and independent variables [29].…”

Section: Discussionmentioning

confidence: 99%

“…These conditions can be primarily affected by soils, vegetation type, long-term climate, or topography. To approximate the site potential, we used a long historical time series of GSN and eliminated years when the growth was reduced to represent the site's potential to produce biomass under good conditions [51]. We define site potential as the mean above the long-term (2000-2016) GSN median.…”

Section: Site Potentialmentioning

confidence: 99%

Monitoring Drought Impact on Annual Forage Production in Semi-Arid Grasslands: A Case Study of Nebraska Sandhills

et al. 2019

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Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha−1*yr−1, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses.

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“…Comparisons with independent field observations and error assessments reported in previous studies suggest improved accuracies that can be partially attributed to: (1) the enhanced temporal revisit times and resolutions of the HLS dataset, which allowed for the development of nearly seamless weekly NDVI data ( Figure 7); (2) consistent usage of sampling protocols across the model testing and training datasets; and/or (3) the use of other relevant ancillary data related to exotic annual grass cover (i.e., phenometrics, soils, topography). Moreover, while there are existing systems that use multi-date satellite imagery to develop fractional cover maps of annual invasive herbaceous cover [19,21,51], such approaches are generally not suited for developing near real-time estimates. These approaches, which are subject to data latency issues or a lack of an automated workflow, have made use of coarse spatial-resolution inputs (e.g., eMODIS), downscaled MODIS products, or composites constructed from Landsat data with coarse temporal granularity (i.e., 16 day), and have not capitalized on the synergistic use of seamless Landsat-8 and Sentinel-2 data for improved characterization of invasive annual grass dynamics.…”

Section: Exotic Annual Grass Cover Modeling Mapping and Validationmentioning

confidence: 99%

“…Emerging technologies and remote sensing sensors have spawned a deluge of information and tools that are useful for characterizing invasive plants and fractional components of dryland communities at local to regional scales [14][15][16]. Previous studies have used information collected by a suite of remote sensing platforms (e.g., Moderate Resolution Image Spectroradiometer (MODIS), Landsat, Advanced Very High Resolution Radiometer (AVHRR)) to quantify spatiotemporal patterns of invasive plant species [17], including cheatgrass cover and die-off [18][19][20][21], and associated drivers of invasion at local and regional scales [17,22,23]. 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].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony

et al. 2020

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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.

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Mapping and Monitoring Cheatgrass Dieoff in Rangelands of the Northern Great Basin, USA

Cited by 31 publications

References 39 publications

Remote Detection of Invasive Alien Species

Remote Detection of Invasive Alien Species

Monitoring Drought Impact on Annual Forage Production in Semi-Arid Grasslands: A Case Study of Nebraska Sandhills

Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony

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