Medusahead Phenology estimates in the Snake River Plain and Northern Basin and Range based on 30-m HLS NDVI (ver. 1.0)

Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. The medusahead phenology in the Snake River Plain (SRP) and Northern Basin and Range (NBR) based on 30m near seamless Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016 and 2022 (Dahal et al., 2022) were processed using a 3 step method. We first identified a set of points to derive an HLS NDVI timeseries based on high probability of cheatgrass and medusahead cover. Second, we extracted the phenological metrics used for training the models by applying a decision tree processing technique on the NDVI timeseries. Finally, we utilized automated machine learning techniques to derive phenological models that were used to develop maps for the entire study area per 30-m pixel. The medusahead phenology model produced three metrics (Start of Season Time (SOST), End of Season Time (EOST), and Maximum Time (MAXT)) and calculated five metrics for identifying the sustained growth characteristics of medusahead throughout SRP and NBR ecoregions. The current suites of 30-m spatial resolution phenological metrics are SOST; Start of Season NDVI (SOSN); EOST; End of Season NDVI (EOSN); Maximum Time (MAXT); Maximum NDVI (MAXN); Duration (DUR); and Amplitude (AMP). Datasets 2017 to 2021 were developed using decision tree analysis training data from their respective year, but 2022 was developed from unseen NDVI datasets to test robustness of the phenology model. References: Dahal, D.; Pastick, N.J.; Boyte, S.P.; Parajuli, S.; Oimoen, M.J.; Megard, L.J. 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, 14, doi:10.3390/rs14040807.

[
  "010:12"
]

[
  "geospatial"
]