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 2021) abundances of exotic annual and perennial grass species throughout the rangelands of the western United States. This monitoring system leverages field observations and remote-sensing data with artificial intelligence/machine learning to rapidly produce annual and early season estimates of species abundances at a 30-m spatial resolution. We introduce a fully automated and multi-task deep-learning framework to simultaneously predict and generate weekly, near-seamless composites of Harmonized Landsat Sentinel-2 spectral data. These data, along with auxiliary datasets and time series metrics, are incorporated into an ensemble of independent XGBoost models. This study demonstrates that inclusion of the Normalized Difference Vegetation Index and Normalized Difference Wetness Index time-series data generated from our deep-learning framework enables near real-time and accurate mapping of EAG (Median Absolute Error (MdAE): 3.22, 2.72, and 0.02; and correlation coefficient (r): 0.82, 0.81, and 0.73; respectively for EAG, cheatgrass, and medusahead) and native perennial grass abundance (MdAE: 2.51, r:0.72 for Sandberg bluegrass (Poa secunda)). Our approach and the resulting data provide insights into rangeland grass dynamics, which will be useful for applications, such as fire and drought monitoring, habitat suitability mapping, as well as land-cover and land-change modelling. Spatially explicit, timely, and accurate species-specific abundance datasets provide invaluable information to land managers.
Citation Information
| Publication Year | 2022 |
|---|---|
| Title | Multi-species inference of exotic annual and native perennial grasses in rangelands of the western United States using Harmonized Landsat and Sentinel-2 data |
| DOI | 10.3390/rs14040807 |
| Authors | Devendra Dahal, Neal J. Pastick, Stephen P. Boyte, Sujan Parajuli, Michael J. Oimoen, Logan J. Megard |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Remote Sensing |
| Index ID | 70228396 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Earth Resources Observation and Science (EROS) Center |
Related Content
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, July 2021, (ver 2.0, January 2022)
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrass in the Sagebrush Biome, USA, 2016 - 2021 (ver. 2.0, December 2022)
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, May 2021, v1
Related Content
- Data
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, July 2021, (ver 2.0, January 2022)
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, BromFractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrass in the Sagebrush Biome, USA, 2016 - 2021 (ver. 2.0, December 2022)
These datasets provide historical (2016 - 2021) estimates of fractional cover for exotic annual grass (EAG) species and a native perennial bunch grass. The study area covers arid and semi-arid rangelands of the western U.S. Four fractional cover maps per year comprise this release, along with the corresponding confidence maps, for: 1) a group of 17 species of EAGs; 2) cheatgrass (Bromus tectorum);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 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 japonic - Connect