Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony
Remote Sensing
Links
- More information: Publisher Index Page (via DOI)
- Data Releases:
- USGS data release - Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020
- USGS data release - Near real time estimation of annual exotic herbaceous fractional cover in the sagebrush ecosystem 30m, USA, July 2020
- USGS data release - Weekly cloud free Harmonized Landsat Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) data for western United States (2016 – 2019).
- USGS data release - Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States (2016 - 2019)
- Open Access Version: Publisher Index Page
- Download citation as: RIS | Dublin Core
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.
Suggested Citation
Pastick, N., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., Wu, Z., 2020, Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony: Remote Sensing, v. 12, no. 4, 725, 17 p.; Data release, https://doi.org/10.3390/rs12040725.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony |
| Series title | Remote Sensing |
| DOI | 10.3390/rs12040725 |
| Volume | 12 |
| Issue | 4 |
| Publication Date | February 22, 2020 |
| Year Published | 2020 |
| Language | English |
| Publisher | MDPI |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 725, 17 p.; Data release |
| Country | United States |
| State | California, Idaho, Nevada, Oregon |