New Dataset: Disturbance probability percentile

[freitagb]

Contact Details

brian.m.freitag@nasa.gov

URL/DOI

No response

Data License Identifier

CC0-1.0

Data Location

s3://veda-data-store-staging/disturbance-probability-percentile/spec_prob_mosaic_2022-10-03_day.tif

Size Estimate

63 MB

Number of Items

1

Description

s33 json

Collection Creation Notebook

collection metadata below

Item Creation Notebook

none

Checklist

• [X] Files are valid COGs. Use rio cogeo validate
• [X] COGs appear to be correct

Any additional info you think is relevant, possibly including spatial or temporal subset if applicable?

{
    "collection": "disturbance-probability-percentile",
    "description": "The UCONN GERS lab developed a near real-time platform ('CONUS Disturbance Watcher') for detecting land disturbances from Harmonized Landsat Sentinel-2 (HLS) dataset. The platform first applied Stochastic Continuous Change Detection (S-CCD) to update spectral change magnitudes and other disturbance features based upon the latest images, and then predicts disturbance probability using the pre-trained models from historical disturbance datasets.",
    "is_periodic": false,
    "license": "CC0-1.0",
    "providers": [
        {
            "name": "Su Ye",
            "description": "Su Ye was a Postdoc associate of GERS lab who developed the core disturbance detection algorithms and built the NRT platform.",
            "roles": [
                "producer",
            ],
            "url": "https://gers.users.earthengine.app/view/nrt-conus"
        },
        {
            "name": "Zhe Zhu",
            "description": "Zhe Zhu is an Assistant Professor in the Department of Natural Resources & the Environment at the University of Connecticut. Zhe led the development of the core disturbanc detection algorithms.",
            "roles": [
                "producer",
            ],
            "url": "https://gers.users.earthengine.app/view/nrt-conus"
        },
        {
            "name": "Global Environmental Remote Sensing Laboratory",
            "description": "The Global Environmental Remote Sensing Laboratory (GERS Lab) at the University of Connecticut uses quantitative remote sensing to understand how the world is changing.",
            "roles": [
                "host",
            ],
            "url": "https://gerslab.uconn.edu/"
        },        
    ],
    "spatial_extent": {
        "bbox": [[-84.132,25.224,-79.853,30.728]]
    },
    "temporal_extent": {
        "interval": ["2022-10-03T00:00:00Z","2022-10-03T23:59:59Z"]
    },
    "time_density": null,
    "title": "Near Real-time Disturbance probability map (%)"
}

To Do

• [x] Open PR for publishing those datasets to the Staging API
• [x] Notify QA / move ticket to QA state
• [x] Once approved, merge and close