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Article

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

1
KBR, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA
2
U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA
3
U.S. Geological Survey, National Land Imaging Program, Flagstaff, AZ 86001, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 725; https://doi.org/10.3390/rs12040725
Received: 4 February 2020 / Revised: 20 February 2020 / Accepted: 20 February 2020 / Published: 22 February 2020
(This article belongs to the Special Issue Remote Sensing of Dryland Environment)
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. View Full-Text
Keywords: data mining; invasive plants; Landsat; Sentinel-2; time-series analysis; phenology data mining; invasive plants; Landsat; Sentinel-2; time-series analysis; phenology
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MDPI and ACS Style

Pastick, N.J.; Dahal, D.; Wylie, B.K.; Parajuli, S.; Boyte, S.P.; Wu, Z. Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony. Remote Sens. 2020, 12, 725. https://doi.org/10.3390/rs12040725

AMA Style

Pastick NJ, Dahal D, Wylie BK, Parajuli S, Boyte SP, Wu Z. Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony. Remote Sensing. 2020; 12(4):725. https://doi.org/10.3390/rs12040725

Chicago/Turabian Style

Pastick, Neal J., Devendra Dahal, Bruce K. Wylie, Sujan Parajuli, Stephen P. Boyte, and Zhouting Wu. 2020. "Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony" Remote Sensing 12, no. 4: 725. https://doi.org/10.3390/rs12040725

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