Conference Presentations by Sujan Parajuli
Papers by Sujan Parajuli
Biological Invasions
This research builds upon the extensive body of work to model exotic annual grass (EAG) character... more This research builds upon the extensive body of work to model exotic annual grass (EAG) characteristics and invasion. EAGs increase wildland fire risk and intensifies wildland fire behavior in western U.S. rangelands. Therefore, understanding characteristics of EAG growth increases understanding of its dynamics and can inform rangeland management decisions. To better understand EAG phenology and spatial distribution, monthly weather (precipitation, minimum and maximum temperature) variables were analyzed for 24 level III ecoregions. This research characterizes EAGs’ phenology identified by a normalized difference vegetation index (NDVI) threshold-based interpolation technique. An EAG phenology metric model was used to estimate a growing season dynamic for the years 2017–2021 for shrub and herbaceous land cover types in the western conterminous United States (66% of the area). The EAG phenology metrics include six growing season metrics such as start of season time, end of season tim...
Biological Invasions
This research builds upon the extensive body of work to model exotic annual grass (EAG) character... more This research builds upon the extensive body of work to model exotic annual grass (EAG) characteristics and invasion. EAGs increase wildland fire risk and intensifies wildland fire behavior in western U.S. rangelands. Therefore, understanding characteristics of EAG growth increases understanding of its dynamics and can inform rangeland management decisions. To better understand EAG phenology and spatial distribution, monthly weather (precipitation, minimum and maximum temperature) variables were analyzed for 24 level III ecoregions. This research characterizes EAGs’ phenology identified by a normalized difference vegetation index (NDVI) threshold-based interpolation technique. An EAG phenology metric model was used to estimate a growing season dynamic for the years 2017–2021 for shrub and herbaceous land cover types in the western conterminous United States (66% of the area). The EAG phenology metrics include six growing season metrics such as start of season time, end of season tim...
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrass in the Sagebrush Biome, USA, 2016 - 2020
These datasets provide historical estimates of fractional cover for some specific exotic annual g... more These datasets provide historical estimates of fractional cover for some specific exotic annual grasses (EAG) species and a native perennial bunch grass for 2016 - 2020. Four specific fractional cover maps per year along with corresponding confidence maps are included in this release. The maps include; 1) Exotic annual grasses (EAG) fractional cover map that includes cheatgrass (Bromus tectrorum) and 16 other species, i.e., Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and medusahead (Taeniatherum caput-medusae). 2) cheatgrass, 3) medusahead, which are highly problematic exotic species in the western U.S. and 4) Sandberg bluegrass (poa secunda), a native perennial bunch grass phenologically similar with exotic annuals leading confusion, are also mapped separately. These datasets...
Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data
Remote Sensing, 2022
The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Tae... more The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at early stages of invasion or growth, can be problematic because of overlapping controls and similar phenological characteristics among native and other exotic vegetation. Subsequently, refining and developing tools capable of quantifying the abundance and phenology of annual and perennial grass species would be beneficial to help inform conservation and management efforts at local to regional scales. Here, we deploy an enhanced version of the U.S. Geological Survey Rangeland Exotic Plant Monitoring System to develop timely and accurate maps of annual (2016–2020) and intra-annual (May 2021 and July...
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, May 2021, v1
This dataset provides early estimates of 2021 exotic annual grasses (EAG) fractional cover predic... more This dataset provides early estimates of 2021 exotic annual grasses (EAG) fractional cover predicted on May 3rd. We develop and release EAG fractional cover map with an emphasis on cheatgrass (Bromus tectrorum) but it also includes number of other species, i.e., Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and medusahead (Taeniatherum caput-medusae. The dataset was generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; Harmonized Landsat and Sentinel-2 (HLS) based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI); other relevant environmental, vegetation, remotely sensed, and geophysical drivers; and artificial intelligence/machine learning techniques. A total ...
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, July 2021
These datasets provide early estimates of 2021 exotic annual grasses (EAG) fractional cover predi... more These datasets provide early estimates of 2021 exotic annual grasses (EAG) fractional cover predicted on July 1 using satellite observation data available until June 28th. In previous releases, we developed and released one general EAG fractional cover map with an emphasis on cheatgrass (Bromus tectrorum) but also included number of other species, i.e., Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and medusahead (Taeniatherum caput-medusae). New to this release, cheatgrass and medusahead, which are highly problematic exotic species in the western U.S., are also mapped separately. These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; Harmonized Landsat and Sentinel-2 (HLS) based No...
Modelled long-term wildfire occurrence probabilities in sagebrush-dominated ecosystems in the western US (1985 to 2019)
Exotic annual grasses are one of the most damaging biological stressors in western North America ... more Exotic annual grasses are one of the most damaging biological stressors in western North America and increase the susceptibility of landscapes to wildfire occurrence. Here we couple estimates of long-term rangeland component fractions (e.g. exotic annual grasses) with remote sensing, climate data, and machine learning techniques to estimate the long-term (1985 to 2019) probability of wildfire occurrence (30-m spatial resolution) in sagebrush-dominated landscapes of the western United States.
Historic and future trends in exotic annual grass (%) cover in the western US (1985 to 2019 and 2025 to 2040)
Exotic annual grasses [EAG] are one of the most damaging biological stressors in western North Am... more Exotic annual grasses [EAG] are one of the most damaging biological stressors in western North America. Despite numerous environmental and societal impacts associated with EAG there remains a need to enhance regional monitoring capabilities to better guide management and conservation efforts. Here we provide estimates of historic and potential future trends in EAG abundance that were developed using linear trend analysis and machine learning techniques at a 30-m spatial resolution. Specifically, these data represent historic (1985 to 2019) and potential future (2025-2040) rates of exotic annual grass change as estimated using Theil-Sen regression and a process-constrained, random forest model assuming only changes in climate under Representative Concentration Pathways (RCP 4.5 and 8.5), respectively.
Fractional estimates of invasive annual grass cover in dryland ecosystems of western United States (2016 - 2019)
The dryland ecosystems of the western United States have been mushroomed by invasive annual grass... more The dryland ecosystems of the western United States have been mushroomed by invasive annual grasses, such as cheatgrass(Bromus tectorum L.), that has promoted increased fire activity and reduced biodiversity detrimental to socio-environmental systems. The use of remote sensing tools to monitor exotic annual grass cover and dynamics over large areas can support early detection and rapid response initiatives.Thus, this dataset was generated using in situ observations, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables (e.g. soils and topography) and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution of 2016-2019. Comparisons with Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) field data (2016 and 2017) indicate good agreement between observed and mapped values (n = 1700; r = 0.83; mean absolute error [MAE] = 11), as constructed from an ensemble of reg...
Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020
The dataset provides an estimate of 2020 herbaceous mostly annual fractional cover predicted on M... more The dataset provides an estimate of 2020 herbaceous mostly annual fractional cover predicted on May 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P9ZEK5M1., Boyte et al. 2018 https://doi.org/10.5066/P9KSR9Z4.), but we are now mapping at a 30m resolution (Pastick et al. 2020 doi:10.3390/rs12040725). This dataset was generated using in situ observations from Bureau of Land Management?s (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; weekly composites of harmonized Landsat and Sentinel-2 (HLS) data (https://hls.gsfc.nasa.gov/); relevant environmental, vegetation, remotely sensed, and geophysical drivers. These data were integrating into regression tree (RT) models for prediction of weekly cloud free Normalized Difference Vegetation Index (NDVI). A total 11,002 AIM plots from years 2016 - 2019 were used to train an ensemble of five-fold RT models using a cros...
Fractional estimates of invasive annual grass cover in dryland ecosystems of western United States (2016 - 2018)
Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ec... more 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. Here, we integrated in situ observations, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables (e.g. soils and topography) and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution from 2016 to 2018. Comparisons with Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) field data (2016 and 2017) indicate good agreement between observed and mapped values (n = 1700; r = 0.83; mean absolute error [MAE] = 11), as constructed from an ensemble of regressio...
Rapid Monitoring of the Abundance and Spread of Exotic Annual Grasses in the Western United States Using Remote Sensing and Machine Learning
Remote Sensing
Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ec... more 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%...
Remote Sensing
Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ec... more 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%...
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Conference Presentations by Sujan Parajuli
Papers by Sujan Parajuli