Difference between revisions of "Near-Global Forest Canopy Heights GLAD"

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(Created page with "Category: Online DataCategory: Forest MapsCategory: InnoWind File:GlobalGLAD_Africa.png|right|thumb|350px|GlobalGLAD Forest Map – Site Near Ouésso, Republic o...")
 
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[[Category: Online Data]][[Category: Forest Maps]][[Category: InnoWind]]
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[[Category: Online Data]][[Category: Forest Maps]]
 
[[File:GlobalGLAD_Africa.png|right|thumb|350px|GlobalGLAD Forest Map – Site Near Ouésso, Republic of the Congo.]]
 
[[File:GlobalGLAD_Africa.png|right|thumb|350px|GlobalGLAD Forest Map – Site Near Ouésso, Republic of the Congo.]]
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== Introduction ==
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The Near Global Forest Canopy Height (GLAD, 2019) is a dataset developed from a fusion of GEDI lidar data (Global Ecosystem Dynamics Investigation Lidar) and Landsat imagery data. This dataset has a near global spatial coverage ranging from latitudes 52.6S to 52.6N. Currently (2020) it is to be considered as a prototype map as both machine learning algorithms and available data are still being being improved. The dataset was developed by the Global Land Analysis and Discovery (GLAD) laboratory in the Department of Geographical Sciences at the University of Maryland. The data reflects the 95% relative height of the canopy. In the current version, tree heights beyond 30m are saturated by the model used.
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=== Usage Notes ===
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# Point 1
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# Point 2
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== Availability from within WindPRO ==
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== Data Type and Use in windPRO ==
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== Coverage ==
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This dataset covers the world, between .
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== License ==
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== Acknowledgement ==
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* Point
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* Point
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== External Links ==
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# Link
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# Link

Revision as of 14:10, 8 September 2020

GlobalGLAD Forest Map – Site Near Ouésso, Republic of the Congo.

Introduction

The Near Global Forest Canopy Height (GLAD, 2019) is a dataset developed from a fusion of GEDI lidar data (Global Ecosystem Dynamics Investigation Lidar) and Landsat imagery data. This dataset has a near global spatial coverage ranging from latitudes 52.6S to 52.6N. Currently (2020) it is to be considered as a prototype map as both machine learning algorithms and available data are still being being improved. The dataset was developed by the Global Land Analysis and Discovery (GLAD) laboratory in the Department of Geographical Sciences at the University of Maryland. The data reflects the 95% relative height of the canopy. In the current version, tree heights beyond 30m are saturated by the model used.

Usage Notes

  1. Point 1
  2. Point 2

Availability from within WindPRO

Data Type and Use in windPRO

Coverage

This dataset covers the world, between .

License

Acknowledgement

  • Point
  • Point

External Links

  1. Link
  2. Link