Disturbance Agents in California, 1985 - 2021
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Raster data indicating the timing (annual), location (30 m resolution) and type of disturbance occurring in California in the years 1985 - 2021. Estimates are derived by applying a change detection algorithm to time series of Landsat data and using machine learning, calibrated against existing geospatial datasets, to classify detected breaks.Legend: 1 is fire, 2 is timber harvest, 3 is drought-induced forest die-off, 4 is unattributed greening, and 5 is unattributed browning. Greening and browning refer to events that trigger a statistical change in remote sensing time series, but is not classified as a disturbance. The fire, harvest, and die-off disturbances mapped are those of relatively high severity.Data are organized into 5000 x 5000 pixel tiles that match the tiling system used for the USGS Analysis Ready Data Landsat Collectio' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["fire, harvest, drought, forest, California"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Raster data indicating the timing (annual), location (30 m resolution) and type of disturbance occurring in California in the years 1985 - 2021. Estimates are derived by applying a change detection algorithm to time series of Landsat data and using machine learning, calibrated against existing geospatial datasets, to classify detected breaks.Legend: 1 is fire, 2 is timber harvest, 3 is drought-induced forest die-off, 4 is unattributed greening, and 5 is unattributed browning. Greening and browning refer to events that trigger a statistical change in remote sensing time series, but is not classified as a disturbance. The fire, harvest, and die-off disturbances mapped are those of relatively high severity.Data are organized into 5000 x 5000 pixel tiles that match the tiling system used for the USGS Analysis Ready Data Landsat Collectio' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Raster data indicating the timing (annual), location (30 m resolution) and type of disturbance occurring in California in the years 1985 - 2021. Estimates are derived by applying a change detection algorithm to time series of Landsat data and using machine learning, calibrated against existing geospatial datasets, to classify detected breaks.Legend: 1 is fire, 2 is timber harvest, 3 is drought-induced forest die-off, 4 is unattributed greening, and 5 is unattributed browning. Greening and browning refer to events that trigger a statistical change in remote sensing time series, but is not classified as a disturbance. The fire, harvest, and die-off disturbances mapped are those of relatively high severity.Data are organized into 5000 x 5000 pixel tiles that match the tiling system used for the USGS Analysis Ready Data Landsat Collectio
fire, harvest, drought, forest, California