| snippet:
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This dataset, assembled by Global Forest Watch, aggregates deforestation alerts from three alert systems (GLAD-L, GLAD-S2, RADD) into a single, integrated deforestation alert layer. This integration allows users to detect deforestation events faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated.
The source alert systems are derived from satellites of varying spectral and spatial resolutions. 30 m GLAD Landsat-based alerts are up-sampled to match the 10 m spatial resolution of Sentinel-based alerts (GLAD-S2, RADD). This avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels.
Alerts are classified as _high_ confidence when detected twice by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or three) of these systems are classified as _highest_ confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system. |
| summary:
|
This dataset, assembled by Global Forest Watch, aggregates deforestation alerts from three alert systems (GLAD-L, GLAD-S2, RADD) into a single, integrated deforestation alert layer. This integration allows users to detect deforestation events faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated.
The source alert systems are derived from satellites of varying spectral and spatial resolutions. 30 m GLAD Landsat-based alerts are up-sampled to match the 10 m spatial resolution of Sentinel-based alerts (GLAD-S2, RADD). This avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels.
Alerts are classified as _high_ confidence when detected twice by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or three) of these systems are classified as _highest_ confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system. |
| extent:
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[[89.8547500003872,25.0465499995076],[92.7879500004336,26.1054499993957]] |
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thumbnail/thumbnail.png |
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1.7976931348623157E308 |
| typeKeywords:
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["ArcGIS","ArcGIS Server","Data","Map Service","Service"] |
| description:
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|
| licenseInfo:
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|
| catalogPath:
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|
| title:
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Meghalaya Deforestation Alerts |
| type:
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Map Service |
| url:
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|
| tags:
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["Meghalaya Deforestation Alerts","meghalaya deforestation alerts","MDA","mda","Deforestation","deforestation","Alerts","alerts","Alert","alert","Global Forest Watch","global forest watch","GFW","gfw"] |
| culture:
|
en-US |
| name:
|
Meghalaya_Deforestation_Alerts |
| guid:
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78526ED3-8982-42BB-AB57-E44EE6BDA549 |
| minScale:
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0 |
| spatialReference:
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WGS_1984_UTM_Zone_46N |